diff --git a/devel/example_devel/instructor/cs108/unitgrade_data/cache.db b/devel/example_devel/instructor/cs108/unitgrade_data/cache.db
new file mode 100644
index 0000000000000000000000000000000000000000..fc9df736c548dca122b6af94c9db8b6f7f6d1b19
Binary files /dev/null and b/devel/example_devel/instructor/cs108/unitgrade_data/cache.db differ
diff --git a/docs/presentation2022/index.tex b/docs/presentation2022/index.tex
index 9d8749197f856ce176a5a44c1910bcc1ae64d2f6..024effd9a59ba5c695f0ac82e42b7d84c15f41e2 100644
--- a/docs/presentation2022/index.tex
+++ b/docs/presentation2022/index.tex
@@ -56,8 +56,12 @@
 \end{itemize}
 \end{frame}
 \begin{frame}
+	\frametitle{Example: Problem 1 from the intro to python course}
 	\begin{itemize}
-		\item ldasf
+		\item Show install: Git pull repository, pip install unitgrade
+		\item Show files. Do simplest example first: Unittest (explicit) and problem script. Use the water height problem for this. Include various hints. Show the exercise description pdf.
+		\item Then show the grade file. Explain this is how they evaluate their homework.
+		\item Then show the unitgrade interface. 
 	\end{itemize}
 \end{frame}
 \begin{frame}
diff --git a/examples/presentation/instructor/cpp_course/tests_ex6.py b/examples/presentation/instructor/cpp_course/tests_ex6.py
index 8cc6174659c097f70516cbf045da3ef8eb51ad8c..37279706929501fa4d17e92beae6a3a97385687a 100644
--- a/examples/presentation/instructor/cpp_course/tests_ex6.py
+++ b/examples/presentation/instructor/cpp_course/tests_ex6.py
@@ -3,7 +3,7 @@ from cpp_course.fractions import from_string, Fraction
 
 class Fractions_from_string(UTestCase):
     def test_from_string_manual(self):
-        self.assertEqual(str(from_string("2 / 3 + 4 / 5")), "22/6")
+        self.assertEqual(str(from_string("2 / 3 + 4 / 5")), "22 / 6")
 
     def test_from_string_smarter(self):
         self.assertEqualC(str(from_string("2 / 3 + 4 / 5")))
diff --git a/examples/presentation/instructor/cpp_course/tests_ex6_grade.py b/examples/presentation/instructor/cpp_course/tests_ex6_grade.py
index 7c7e2a4862eeeb9ff895be658668664128d8b954..fd11dbb77c1ef8459f783a76addc835596e4ecd1 100644
--- a/examples/presentation/instructor/cpp_course/tests_ex6_grade.py
+++ b/examples/presentation/instructor/cpp_course/tests_ex6_grade.py
@@ -1,4 +1,4 @@
 # cpp_course/tests_ex6.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/instructor/cpp_course/unitgrade_data/Fractions_Basics.pkl b/examples/presentation/instructor/cpp_course/unitgrade_data/Fractions_Basics.pkl
index 399069ef78aabbd5c563ea3c29de826589501450..2d31d464ed020fd09e00880bef601fc4392ecb9e 100644
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diff --git a/examples/presentation/instructor/cpp_course/unitgrade_data/Fractions_from_string.pkl b/examples/presentation/instructor/cpp_course/unitgrade_data/Fractions_from_string.pkl
index b01491f2121c122ac1e340a69286cd1a20b39b59..a0e39d1e2fab811d26708601122b4ec6c5dd3ec2 100644
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diff --git a/examples/presentation/instructor/cpp_course/unitgrade_data/cache.db b/examples/presentation/instructor/cpp_course/unitgrade_data/cache.db
index 95dc06a9fec7ebbf6d2fc0e8bcd9122012a98401..6249322ac7b0247001b858a6cd1e4a8338defbd8 100644
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diff --git a/examples/presentation/instructor/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl b/examples/presentation/instructor/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl
index d9f67346343978b015fde99b56b6ab5c85017ca5..77cbbfcab4b630fddc890be3158e1cf5b05050b8 100644
Binary files a/examples/presentation/instructor/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl and b/examples/presentation/instructor/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl differ
diff --git a/examples/presentation/instructor/cpp_exam/tests_exam_grade.py b/examples/presentation/instructor/cpp_exam/tests_exam_grade.py
index 6aa0d1911e814bbe3106605c7a6a01d9c5b5a669..d09bb1329d42fc29cd42b16524e54449d5669ae7 100644
--- a/examples/presentation/instructor/cpp_exam/tests_exam_grade.py
+++ b/examples/presentation/instructor/cpp_exam/tests_exam_grade.py
@@ -1,4 +1,4 @@
 # cpp_exam/tests_exam.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/instructor/cpp_exam/unitgrade_data/Q1Vectors.pkl b/examples/presentation/instructor/cpp_exam/unitgrade_data/Q1Vectors.pkl
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diff --git a/examples/presentation/instructor/cpp_exam/unitgrade_data/Q2RLE.pkl b/examples/presentation/instructor/cpp_exam/unitgrade_data/Q2RLE.pkl
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diff --git a/examples/presentation/instructor/cpp_exam/unitgrade_data/Q3Groceries.pkl b/examples/presentation/instructor/cpp_exam/unitgrade_data/Q3Groceries.pkl
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diff --git a/examples/presentation/instructor/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl b/examples/presentation/instructor/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl
index 46b03afee78f6a519772733cb725d16b7c852dc8..8544aeb6c5ac91153ee70063ef229218461e7344 100644
Binary files a/examples/presentation/instructor/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl and b/examples/presentation/instructor/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl differ
diff --git a/examples/presentation/instructor/deploy.py b/examples/presentation/instructor/deploy.py
index 7877f57ece4dedf9fa180ab05c00178150c28751..08047525f884b01614b02b41be4615207b590972 100644
--- a/examples/presentation/instructor/deploy.py
+++ b/examples/presentation/instructor/deploy.py
@@ -2,12 +2,19 @@ from unitgrade_private import setup_grade_file_report
 from snipper import snip_dir
 from cpp_course.tests_ex6 import Week6
 from cpp_exam.tests_exam import ExamMay2021
+from intro_python.exam_complete import Exam2021
 
-
+# def setup_grade_file_report_hidden(Exam2021, name_without_hidden=None):
+# setup_grade_file_report(Exam2021)  # Create report3_complete_grade.py which tests everything
 
 if __name__ == "__main__":
-    from intro_python.exam import Exam2021
-    setup_grade_file_report(Week6, with_coverage=True, minify=False, obfuscate=False,bzip=True)
-    setup_grade_file_report(ExamMay2021, with_coverage=True, minify=False, obfuscate=False,bzip=True)
-    setup_grade_file_report(Exam2021, with_coverage=True, minify=False, obfuscate=False,bzip=True)
-    snip_dir("./", "../students", clean_destination_dir=True, exclude=['*.token', 'deploy.py'], output_dir="../Latex/output")
+    setup_grade_file_report(Exam2021, remove_hidden=True)
+    setup_grade_file_report(Week6)
+    setup_grade_file_report(ExamMay2021)
+
+    # setup_grade_file_report(Exam2021)
+    # student_directory = "../../students/cs103"
+    # snip_dir("./", student_directory, exclude=['*.token', 'deploy.py', 'report3_complete*.py', '.*'])  # !s
+    snip_dir("./", "../students", clean_destination_dir=True, exclude=['*.token', 'deploy.py', '*_complete*'], output_dir="../Latex/output")
+
+
diff --git a/examples/presentation/instructor/intro_python/exam.py b/examples/presentation/instructor/intro_python/exam.py
index 02b54dc010ec0b19a666967b5b4adf2978a5cbb6..f1d22b97f9bd8f98272e03e1efad41fc26a68b3a 100644
--- a/examples/presentation/instructor/intro_python/exam.py
+++ b/examples/presentation/instructor/intro_python/exam.py
@@ -11,15 +11,6 @@ class Q1_WaterHeight(UTestCase):
         print("Water height computed to be", h, "should be", self.get_expected_test_value())
         self.assertEqual(h, 3.0) # Check the height is 3.0
 
-    @hide
-    def test_water_height_hidden(self):
-        checks = [(120, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
-                  (12, []), (14.2, [8.8]), (0, [0.8]),
-                  (3, [0, 1, 2, 0, 0, 5.2, 6.5, 7.1, 0, 0, 0.1, 1, 2.9, 0.13, 0, 2, 0, 0]),
-                  (0, [0, 5, 2, 0, 0, 5.2, 6.5, 7.1, 0, 0, 0.1, 1, 2.9, 0.13, 0, 1.2, 0, 1.2, 7.5, 0]),
-                  (0, [0, 0, 2, 2.1, 2.4, 2.2, 2.5]), (18, [30, 1, 28.8]), (1, [0.5]), (2, [])]
-        for h0, r in checks:
-            self.assertEqualC(water_height(h0, r))
 
 class Q2_AstronomicalSeason(UTestCase):
     def test_seasons(self):
@@ -27,10 +18,6 @@ class Q2_AstronomicalSeason(UTestCase):
         print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
         self.assertEqualC(season)
 
-    @hide
-    def test_seasons_hidden(self):
-        for d in ['27/12-1998', '21/06-2108', '08/05-1998', '07/08-1945', '22/12-1208', '19/03-2001', '23/09-2018', '21/06-2008','12/04-1964', '13/01-1900']:
-            self.assertEqualC(astronomical_season(d))
 
 
 class Q3_TimeAngle(UTestCase):
@@ -39,11 +26,6 @@ class Q3_TimeAngle(UTestCase):
         print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
         self.assertEqualC(a)
 
-    @hide
-    def test_angle_extended(self):
-        for minute in [0, 15, 18, 20, 34, 50, 59]:
-            for hour in [0, 1, 5, 6, 10, 12]:
-                self.assertEqualC(time_angle(hour, minute))
 
 class Q4_TicTacToe(UTestCase):
     def test_tic_tac(self):
@@ -54,21 +36,6 @@ class Q4_TicTacToe(UTestCase):
         print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
         self.assertEqualC(score)
 
-    @hide
-    def test_tic_tac_hidden(self):
-        boards = [[[1, 2, 0], [1, 2, 0], [1, 2, 0]],
-                  [[1, 1, 1], [2, 1, 2], [2, 2, 1]],
-                  [[2, 0, 1], [2, 1, 0], [0, 0, 2]],
-                  [[1, 0, 2], [0, 1, 0], [2, 0, 1]],
-                  [[2, 0, 1], [0, 2, 1], [0, 0, 1]],
-                  [[0, 1, 0], [0, 1, 1], [2, 2, 2]],
-                  [[1, 1, 2], [0, 2, 0], [2, 1, 0]],
-                  [[1, 1, 1], [0, 2, 0], [0, 0, 0]],
-                  [[1, 2, 1], [2, 1, 0], [2, 0, 1]],
-                  [[0, 0, 0], [0, 1, 0], [0, 0, 0]],
-                  [[2, 1, 1], [1, 1, 2], [2, 0, 0]]]
-        for board in boards:
-            self.assertEqualC(tictactoe(np.asarray(board)))
 
 
 class Q5_StandardizeAddress(UTestCase):
@@ -77,11 +44,6 @@ class Q5_StandardizeAddress(UTestCase):
         print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
         self.assertEqualC(s)
 
-    @hide
-    def test_standardize_address_hidden(self):
-        for address in ['Kongens_Lyngby_2800', '10000_Zagreb','43500 Daruvar','Egtved_6040','Vejle 7200', '02108_Boston',
-                  'Pasadena_91001', '90001_Los_Angeles', 'San_Francisco_94016', 'Rio_de_Jainero_22775']:
-            self.assertEqualC(standardize_address(address))
 
 
 
@@ -96,4 +58,4 @@ class Exam2021(Report):
 
 if __name__ == "__main__":
     from unitgrade import evaluate_report_student
-    evaluate_report_student(Exam2021())
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/instructor/intro_python/exam_complete.py b/examples/presentation/instructor/intro_python/exam_complete.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8f63f3da5d2f0a85d4fb4925843c736c405e73c
--- /dev/null
+++ b/examples/presentation/instructor/intro_python/exam_complete.py
@@ -0,0 +1,93 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check that the height is 3.0
+
+    @hide
+    def test_water_height_hidden(self):
+        checks = [(120, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
+                  (12, []), (14.2, [8.8]), (0, [0.8]),
+                  (3, [0, 1, 2, 0, 0, 5.2, 6.5, 7.1, 0, 0, 0.1, 1, 2.9, 0.13, 0, 2, 0, 0]),
+                  (0, [0, 5, 2, 0, 0, 5.2, 6.5, 7.1, 0, 0, 0.1, 1, 2.9, 0.13, 0, 1.2, 0, 1.2, 7.5, 0]),
+                  (0, [0, 0, 2, 2.1, 2.4, 2.2, 2.5]), (18, [30, 1, 28.8]), (1, [0.5]), (2, [])]
+        for h0, r in checks:
+            self.assertEqualC(water_height(h0, r))
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+    @hide
+    def test_seasons_hidden(self):
+        for d in ['27/12-1998', '21/06-2108', '08/05-1998', '07/08-1945', '22/12-1208', '19/03-2001', '23/09-2018', '21/06-2008','12/04-1964', '13/01-1900']:
+            self.assertEqualC(astronomical_season(d))
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+    @hide
+    def test_angle_extended(self):
+        for minute in [0, 15, 18, 20, 34, 50, 59]:
+            for hour in [0, 1, 5, 6, 10, 12]:
+                self.assertEqualC(time_angle(hour, minute))
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+    @hide
+    def test_tic_tac_hidden(self):
+        boards = [[[1, 2, 0], [1, 2, 0], [1, 2, 0]], [[1, 1, 1], [2, 1, 2], [2, 2, 1]],
+                  [[2, 0, 1], [2, 1, 0], [0, 0, 2]], [[1, 0, 2], [0, 1, 0], [2, 0, 1]],
+                  [[2, 0, 1], [0, 2, 1], [0, 0, 1]], [[0, 1, 0], [0, 1, 1], [2, 2, 2]],
+                  [[1, 1, 2], [0, 2, 0], [2, 1, 0]], [[1, 1, 1], [0, 2, 0], [0, 0, 0]],
+                  [[1, 2, 1], [2, 1, 0], [2, 0, 1]], [[0, 0, 0], [0, 1, 0], [0, 0, 0]]]
+        for board in boards:
+            self.assertEqualC(tictactoe(np.asarray(board)))
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+    @hide
+    def test_standardize_address_hidden(self):
+        for address in ['Kongens_Lyngby_2800', '10000_Zagreb','43500 Daruvar','Egtved_6040','Vejle 7200', '02108_Boston',
+                  'Pasadena_91001', '90001_Los_Angeles', 'San_Francisco_94016', 'Rio_de_Jainero_22775']:
+            self.assertEqualC(standardize_address(address))
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
diff --git a/examples/presentation/instructor/intro_python/exam_complete_grade.py b/examples/presentation/instructor/intro_python/exam_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..60e63ee51618d8d5412a9f69875a70b883f82068
--- /dev/null
+++ b/examples/presentation/instructor/intro_python/exam_complete_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/instructor/intro_python/exam_grade.py b/examples/presentation/instructor/intro_python/exam_grade.py
index b5478483bcc6f0eb8a174cd68889350a6681af80..67b272c3fa4478273b3f9ea2a69d03e824a5ae17 100644
--- a/examples/presentation/instructor/intro_python/exam_grade.py
+++ b/examples/presentation/instructor/intro_python/exam_grade.py
@@ -1,4 +1,4 @@
 # intro_python/exam.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/Q1_WaterHeight.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/Q1_WaterHeight.pkl
index 959b3da92d0109166386114384e2283c87bb0c86..834d08b7bbdcf8ebf2e06cfe57953e687871396a 100644
Binary files a/examples/presentation/instructor/intro_python/unitgrade_data/Q1_WaterHeight.pkl and b/examples/presentation/instructor/intro_python/unitgrade_data/Q1_WaterHeight.pkl differ
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl
index ded5dd0d2b52198997ef58794fc3ffaec0fe37b7..2d677c3561d435b085828c376c30118f918b7c5d 100644
Binary files a/examples/presentation/instructor/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl and b/examples/presentation/instructor/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl differ
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/Q3_TimeAngle.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/Q3_TimeAngle.pkl
index 50090e6cc4db269029576997d4da40c4176dd3c2..8528aeef3c8c029a9a8af2b1bc198d19a0463555 100644
Binary files a/examples/presentation/instructor/intro_python/unitgrade_data/Q3_TimeAngle.pkl and b/examples/presentation/instructor/intro_python/unitgrade_data/Q3_TimeAngle.pkl differ
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/Q4_TicTacToe.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/Q4_TicTacToe.pkl
index 8c6eb03977b19b14b4341b68c8a96b0b1baa5808..163e3b6bbf380c2ec67ca64c32d89bee735a97be 100644
Binary files a/examples/presentation/instructor/intro_python/unitgrade_data/Q4_TicTacToe.pkl and b/examples/presentation/instructor/intro_python/unitgrade_data/Q4_TicTacToe.pkl differ
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl
index e013c45a0f8b0e56fbf974cd7ff8bba3583dfe5d..a55324933d96a979dce39176c27427e4ef8b5ea2 100644
Binary files a/examples/presentation/instructor/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl and b/examples/presentation/instructor/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl differ
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam.artifacts.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam.artifacts.pkl
index e45319db9112fdae5426c258ccf3b595dcb2df0f..d323d4cf41dd5b63d5f45f495924e2f57c13bc78 100644
Binary files a/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam.artifacts.pkl and b/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam.artifacts.pkl differ
diff --git a/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam_complete.artifacts.pkl b/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam_complete.artifacts.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..4eae7864baeb66600ae66130ec025c1fad02d574
Binary files /dev/null and b/examples/presentation/instructor/intro_python/unitgrade_data/main_config_exam_complete.artifacts.pkl differ
diff --git a/examples/presentation/setup_presentation.py b/examples/presentation/setup_presentation.py
index 3d73eecdb95db483646f53af4f83806765b9b1d6..b62af3eec37d00cd6c5e27de7e53a0aa5d85f72c 100644
--- a/examples/presentation/setup_presentation.py
+++ b/examples/presentation/setup_presentation.py
@@ -1,15 +1,8 @@
 import os
 
 if __name__ == "__main__":
-
-
-    from unitgrade_private.pipelines.process_65 import process_by_zip_file
     from unitgrade_private.pipelines.dummy_handins import make_dummies
-
     cdir = os.path.dirname(__file__)
-
-    make_dummies(zip_file_path=cdir + "/handin/project1.zip", n_handins=3, screwups=4, student_base_dir=cdir+"/students", student_grade_file=cdir+"/students/intro_python/exam_grade.py")
-    # student base directory.
-
-
-    pass
\ No newline at end of file
+    make_dummies(zip_file_path=cdir + "/student_handins/intro_python_exam.zip", n_handins=10, screwups=4, student_base_dir=cdir+"/students",
+                 instructor_base_dir=cdir + "/instructor",
+                 student_grade_file=cdir+"/students/intro_python/exam_grade.py")
diff --git a/examples/presentation/student_handins/docker_tango_python/Dockerfile b/examples/presentation/student_handins/docker_tango_python/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..e081c74465e0b3f3a0e933f16f5a1f180c8740c3
--- /dev/null
+++ b/examples/presentation/student_handins/docker_tango_python/Dockerfile
@@ -0,0 +1,40 @@
+# syntax=docker/dockerfile:1
+
+FROM python:3.8-slim-buster
+MAINTAINER Autolab Team <autolab-dev@andrew.cmu.edu>
+
+RUN apt-get update && apt-get install -y \
+  build-essential \
+  gcc \
+  git \
+  make \
+  sudo \
+  python \
+  procps \
+  && rm -rf /var/lib/apt/lists/*
+
+# Install autodriver
+WORKDIR /home
+RUN useradd autolab
+RUN useradd autograde
+RUN mkdir autolab autograde output
+RUN chown autolab:autolab autolab
+RUN chown autolab:autolab output
+RUN chown autograde:autograde autograde
+RUN git clone --depth 1 https://github.com/autolab/Tango.git
+WORKDIR Tango/autodriver
+RUN make clean && make
+RUN cp autodriver /usr/bin/autodriver
+RUN chmod +s /usr/bin/autodriver
+
+# Do the python stuff.
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Clean up
+WORKDIR /home
+RUN apt-get remove -y git && apt-get -y autoremove && rm -rf Tango/
+
+# Check installation
+RUN ls -l /home
+RUN which autodriver
diff --git a/examples/presentation/student_handins/docker_tango_python/requirements.txt b/examples/presentation/student_handins/docker_tango_python/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..70d4fad399affbd2faad24426948fa773d85536d
--- /dev/null
+++ b/examples/presentation/student_handins/docker_tango_python/requirements.txt
@@ -0,0 +1,9 @@
+numpy
+tqdm
+jinja2
+tabulate
+pyfiglet
+colorama
+unitgrade>=0.1.23
+unitgrade-devel>=0.1.37 # Required to run automatic evaluation (load tokens etc.)
+requests # For unitgrade, may remove later.
diff --git a/examples/presentation/student_handins/intro_python_exam.zip b/examples/presentation/student_handins/intro_python_exam.zip
new file mode 100644
index 0000000000000000000000000000000000000000..80aa92c794a66cc63b72332d2002412b19949045
Binary files /dev/null and b/examples/presentation/student_handins/intro_python_exam.zip differ
diff --git a/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token b/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..1a0ef426f0359ec6fd12754b46385cba59ec162f
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token	
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
+0dcd42292c9bd80c13f72336d4f23477a2a820d658f5e7db02fa7a64643fca9057801c426f41331c2ea82045db6240c2f17da1521ad44b0e17e128fc5bbbb7fa 36456
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token b/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..68797b0b56e3e0418752dee0d9f778ccfac9ae26
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token	
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token b/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..74ab5a3d17172fbf232bac79b3b1e04cd6252cc7
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/raw/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token	
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+<html>
+<head>
+<title>Moss Results</title>
+</head>
+<body>
+Moss Results<p>
+Wed Oct 12 07:50:30 PDT 2022
+</p><p>
+Options -l python -m 10
+</p><hr/>
+[ <a href="http://moss.stanford.edu/general/format.html" target="_top"> How to Read the Results</a> | <a href="http://moss.stanford.edu/general/tips.html" target="_top"> Tips</a> | <a href="http://moss.stanford.edu/general/faq.html"> FAQ</a> | <a href="mailto:moss-request@cs.stanford.edu">Contact</a> | <a href="http://moss.stanford.edu/general/scripts.html">Submission Scripts</a> | <a href="http://moss.stanford.edu/general/credits.html" target="_top"> Credits</a> ]
+<hr/>
+<table>
+<tr><th>File 1</th><th>File 2</th><th>Lines Matched
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+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match0-1.html#0" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format &lt;dd&gt; &lt;mm&gt;, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm&lt;3 or (mm==3 and dd&lt;20):
+        season = 'winter'
+    elif mm&lt;6 or (mm==6 and dd&lt;21):
+        season = 'spring'
+    elif mm&lt;9 or (mm==9 and dd&lt;23):
+        season = 'summer'
+    elif mm&lt;12 or (mm==12 and dd&lt;21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+</font>    assert(False)
+<a name="1"></a><font color="#00FF00"><a href="match0-1.html#1" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_31.gif"/></a>
+
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+</font><a name="2"></a><font color="#0000FF"><a href="match0-1.html#2" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_22.gif"/></a>
+
+    if d &lt; 0 or d &gt; 1 or (ones_wins + twos_wins) &gt; 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+</font><a name="3"></a><font color="#00FFFF"><a href="match0-1.html#3" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_10.gif"/></a>
+
+    if a &lt; 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
+</font></pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match0-1.html b/examples/presentation/student_handins/intro_python_exam/report/match0-1.html
new file mode 100644
index 0000000000000000000000000000000000000000..a1936366be875b72e2664cfb56503ebb3736f643
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match0-1.html
@@ -0,0 +1,105 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match0-0.html#0" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format &lt;dd&gt; &lt;mm&gt;, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm&lt;3 or (mm==3 and dd&lt;20):
+        season = 'winter'
+    elif mm&lt;6 or (mm==6 and dd&lt;21):
+        season = 'spring'
+    elif mm&lt;9 or (mm==9 and dd&lt;23):
+        season = 'summer'
+    elif mm&lt;12 or (mm==12 and dd&lt;21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+</font><a name="1"></a><font color="#00FF00"><a href="match0-0.html#1" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_31.gif"/></a>
+
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+</font>    assert(False)
+<a name="2"></a><font color="#0000FF"><a href="match0-0.html#2" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_22.gif"/></a>
+
+    if d &lt; 0 or d &gt; 1 or (ones_wins + twos_wins) &gt; 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+</font><a name="3"></a><font color="#00FFFF"><a href="match0-0.html#3" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_10.gif"/></a>
+
+    assert(False)
+    if a &lt; 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
+</font></pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match0-top.html b/examples/presentation/student_handins/intro_python_exam/report/match0-top.html
new file mode 100644
index 0000000000000000000000000000000000000000..5c2cdaa1204eb952e100745a1b6b0357aa5a415a
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match0-top.html
@@ -0,0 +1,21 @@
+<html>
+<head>
+<title>Top</title>
+</head><body bgcolor="white"><center><table bgcolor="#d0d0d0" border="1" cellspacing="0"><tr><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py (98%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_98.gif"/></th><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py (97%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_97.gif"/></th><th>
+</th></tr><tr><td><a href="match0-0.html#0" name="0" target="0">1-30</a>
+</td><td><a href="match0-0.html#0" name="0" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+</td><td><a href="match0-1.html#0" name="0" target="1">1-30</a>
+</td><td><a href="match0-1.html#0" name="0" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+</td></tr><tr><td><a href="match0-0.html#1" name="1" target="0">32-50</a>
+</td><td><a href="match0-0.html#1" name="1" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_31.gif"/></a>
+</td><td><a href="match0-1.html#1" name="1" target="1">31-49</a>
+</td><td><a href="match0-1.html#1" name="1" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_31.gif"/></a>
+</td></tr><tr><td><a href="match0-0.html#2" name="2" target="0">51-72</a>
+</td><td><a href="match0-0.html#2" name="2" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_22.gif"/></a>
+</td><td><a href="match0-1.html#2" name="2" target="1">51-72</a>
+</td><td><a href="match0-1.html#2" name="2" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_22.gif"/></a>
+</td></tr><tr><td><a href="match0-0.html#3" name="3" target="0">72-86</a>
+</td><td><a href="match0-0.html#3" name="3" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_10.gif"/></a>
+</td><td><a href="match0-1.html#3" name="3" target="1">73-87</a>
+</td><td><a href="match0-1.html#3" name="3" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_10.gif"/></a>
+</td></tr></table></center></body></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match0.html b/examples/presentation/student_handins/intro_python_exam/report/match0.html
new file mode 100644
index 0000000000000000000000000000000000000000..93f3ff0df374f495e1a7b93b26519a8c5c0d90a8
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match0.html
@@ -0,0 +1,3 @@
+<html>
+<head><title>Matches for /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py and /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py</title>
+</head><frameset rows="150,*"><frameset cols="1000,*"><frame frameborder="0" name="top" src="match0-top.html"/></frameset><frameset cols="50%,50%"><frame name="0" src="match0-0.html"/><frame name="1" src="match0-1.html"/></frameset></frameset></html>
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diff --git a/examples/presentation/student_handins/intro_python_exam/report/match1-0.html b/examples/presentation/student_handins/intro_python_exam/report/match1-0.html
new file mode 100644
index 0000000000000000000000000000000000000000..c91324b5bb43a69b60fe295016c37b2ccab70378
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match1-0.html
@@ -0,0 +1,106 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match1-1.html#0" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format &lt;dd&gt; &lt;mm&gt;, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm&lt;3 or (mm==3 and dd&lt;20):
+        season = 'winter'
+    elif mm&lt;6 or (mm==6 and dd&lt;21):
+        season = 'spring'
+    elif mm&lt;9 or (mm==9 and dd&lt;23):
+        season = 'summer'
+    elif mm&lt;12 or (mm==12 and dd&lt;21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+</font>    assert(False)
+<a name="2"></a><font color="#0000FF"><a href="match1-1.html#2" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_15.gif"/></a>
+
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+</font><a name="3"></a><font color="#00FFFF"><a href="match1-1.html#3" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_12.gif"/></a>
+
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+</font><a name="1"></a><font color="#00FF00"><a href="match1-1.html#1" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_20.gif"/></a>
+
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d &lt; 0 or d &gt; 1 or (ones_wins + twos_wins) &gt; 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+</font><a name="4"></a><font color="#FF00FF"><a href="match1-1.html#4" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_4_10.gif"/></a>
+
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a &lt; 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+</font>    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
+</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match1-1.html b/examples/presentation/student_handins/intro_python_exam/report/match1-1.html
new file mode 100644
index 0000000000000000000000000000000000000000..252d2a8e03a3b582764f20ba86a1340c0913fcd0
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match1-1.html
@@ -0,0 +1,109 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match1-0.html#0" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format &lt;dd&gt; &lt;mm&gt;, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm&lt;3 or (mm==3 and dd&lt;20):
+        season = 'winter'
+    elif mm&lt;6 or (mm==6 and dd&lt;21):
+        season = 'spring'
+    elif mm&lt;9 or (mm==9 and dd&lt;23):
+        season = 'summer'
+    elif mm&lt;12 or (mm==12 and dd&lt;21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+</font><a name="2"></a><font color="#0000FF"><a href="match1-0.html#2" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_15.gif"/></a>
+
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+</font>        assert(False)
+<a name="3"></a><font color="#00FFFF"><a href="match1-0.html#3" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_12.gif"/></a>
+
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+</font>    assert(False)
+<a name="1"></a><font color="#00FF00"><a href="match1-0.html#1" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_20.gif"/></a>
+
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d &lt; 0 or d &gt; 1 or (ones_wins + twos_wins) &gt; 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+</font>    assert(False)
+<a name="4"></a><font color="#FF00FF"><a href="match1-0.html#4" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_4_10.gif"/></a>
+
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a &lt; 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+</font>    h = h0 #!b
+    assert(False)
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
+</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match1-top.html b/examples/presentation/student_handins/intro_python_exam/report/match1-top.html
new file mode 100644
index 0000000000000000000000000000000000000000..76e1aff1787f0511c5995b287d96589b677358f5
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match1-top.html
@@ -0,0 +1,25 @@
+<html>
+<head>
+<title>Top</title>
+</head><body bgcolor="white"><center><table bgcolor="#d0d0d0" border="1" cellspacing="0"><tr><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py (93%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_93.gif"/></th><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py (92%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_92.gif"/></th><th>
+</th></tr><tr><td><a href="match1-0.html#0" name="0" target="0">1-30</a>
+</td><td><a href="match1-0.html#0" name="0" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+</td><td><a href="match1-1.html#0" name="0" target="1">1-30</a>
+</td><td><a href="match1-1.html#0" name="0" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_33.gif"/></a>
+</td></tr><tr><td><a href="match1-0.html#1" name="1" target="0">50-69</a>
+</td><td><a href="match1-0.html#1" name="1" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_20.gif"/></a>
+</td><td><a href="match1-1.html#1" name="1" target="1">51-70</a>
+</td><td><a href="match1-1.html#1" name="1" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_20.gif"/></a>
+</td></tr><tr><td><a href="match1-0.html#2" name="2" target="0">32-42</a>
+</td><td><a href="match1-0.html#2" name="2" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_15.gif"/></a>
+</td><td><a href="match1-1.html#2" name="2" target="1">31-41</a>
+</td><td><a href="match1-1.html#2" name="2" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_15.gif"/></a>
+</td></tr><tr><td><a href="match1-0.html#3" name="3" target="0">43-49</a>
+</td><td><a href="match1-0.html#3" name="3" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_12.gif"/></a>
+</td><td><a href="match1-1.html#3" name="3" target="1">43-49</a>
+</td><td><a href="match1-1.html#3" name="3" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_12.gif"/></a>
+</td></tr><tr><td><a href="match1-0.html#4" name="4" target="0">70-82</a>
+</td><td><a href="match1-0.html#4" name="4" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_4_10.gif"/></a>
+</td><td><a href="match1-1.html#4" name="4" target="1">72-84</a>
+</td><td><a href="match1-1.html#4" name="4" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_4_10.gif"/></a>
+</td></tr></table></center></body></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match1.html b/examples/presentation/student_handins/intro_python_exam/report/match1.html
new file mode 100644
index 0000000000000000000000000000000000000000..d42076d006f72b8e698b4776164115c968d3d0a2
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match1.html
@@ -0,0 +1,3 @@
+<html>
+<head><title>Matches for /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py and /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py</title>
+</head><frameset rows="150,*"><frameset cols="1000,*"><frame frameborder="0" name="top" src="match1-top.html"/></frameset><frameset cols="50%,50%"><frame name="0" src="match1-0.html"/><frame name="1" src="match1-1.html"/></frameset></frameset></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match2-0.html b/examples/presentation/student_handins/intro_python_exam/report/match2-0.html
new file mode 100644
index 0000000000000000000000000000000000000000..69c7c780fd9d1e4413353a4cfff1c4a52f249a33
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match2-0.html
@@ -0,0 +1,105 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match2-1.html#0" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_49.gif"/></a>
+
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format &lt;dd&gt; &lt;mm&gt;, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm&lt;3 or (mm==3 and dd&lt;20):
+        season = 'winter'
+    elif mm&lt;6 or (mm==6 and dd&lt;21):
+        season = 'spring'
+    elif mm&lt;9 or (mm==9 and dd&lt;23):
+        season = 'summer'
+    elif mm&lt;12 or (mm==12 and dd&lt;21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+</font><a name="2"></a><font color="#0000FF"><a href="match2-1.html#2" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_12.gif"/></a>
+
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+</font>    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    assert(False)
+<a name="1"></a><font color="#00FF00"><a href="match2-1.html#1" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_18.gif"/></a>
+
+    if d &lt; 0 or d &gt; 1 or (ones_wins + twos_wins) &gt; 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+</font>    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+<a name="3"></a><font color="#00FFFF"><a href="match2-1.html#3" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_7.gif"/></a>
+
+    assert(False)
+    if a &lt; 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+</font>    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
+</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match2-1.html b/examples/presentation/student_handins/intro_python_exam/report/match2-1.html
new file mode 100644
index 0000000000000000000000000000000000000000..79130271de805c82855f220f893e798ec69b0c28
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match2-1.html
@@ -0,0 +1,107 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match2-0.html#0" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_49.gif"/></a>
+
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format &lt;dd&gt; &lt;mm&gt;, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm&lt;3 or (mm==3 and dd&lt;20):
+        season = 'winter'
+    elif mm&lt;6 or (mm==6 and dd&lt;21):
+        season = 'spring'
+    elif mm&lt;9 or (mm==9 and dd&lt;23):
+        season = 'summer'
+    elif mm&lt;12 or (mm==12 and dd&lt;21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+</font>        assert(False)
+<a name="2"></a><font color="#0000FF"><a href="match2-0.html#2" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_12.gif"/></a>
+
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+</font>    assert(False)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+<a name="1"></a><font color="#00FF00"><a href="match2-0.html#1" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_18.gif"/></a>
+
+    if d &lt; 0 or d &gt; 1 or (ones_wins + twos_wins) &gt; 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+</font>    assert(False)
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+<a name="3"></a><font color="#00FFFF"><a href="match2-0.html#3" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_7.gif"/></a>
+
+    a = hour_hand - minute_hand
+    if a &lt; 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+</font>    h = h0 #!b
+    assert(False)
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
+</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match2-top.html b/examples/presentation/student_handins/intro_python_exam/report/match2-top.html
new file mode 100644
index 0000000000000000000000000000000000000000..6c314ff624d390e1252814e7f3b306f2dd0c608e
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match2-top.html
@@ -0,0 +1,21 @@
+<html>
+<head>
+<title>Top</title>
+</head><body bgcolor="white"><center><table bgcolor="#d0d0d0" border="1" cellspacing="0"><tr><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py (87%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_87.gif"/></th><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py (87%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_87.gif"/></th><th>
+</th></tr><tr><td><a href="match2-0.html#0" name="0" target="0">1-41</a>
+</td><td><a href="match2-0.html#0" name="0" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_49.gif"/></a>
+</td><td><a href="match2-1.html#0" name="0" target="1">1-41</a>
+</td><td><a href="match2-1.html#0" name="0" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_49.gif"/></a>
+</td></tr><tr><td><a href="match2-0.html#1" name="1" target="0">51-69</a>
+</td><td><a href="match2-0.html#1" name="1" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_18.gif"/></a>
+</td><td><a href="match2-1.html#1" name="1" target="1">52-70</a>
+</td><td><a href="match2-1.html#1" name="1" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_18.gif"/></a>
+</td></tr><tr><td><a href="match2-0.html#2" name="2" target="0">42-48</a>
+</td><td><a href="match2-0.html#2" name="2" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_12.gif"/></a>
+</td><td><a href="match2-1.html#2" name="2" target="1">43-49</a>
+</td><td><a href="match2-1.html#2" name="2" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_12.gif"/></a>
+</td></tr><tr><td><a href="match2-0.html#3" name="3" target="0">73-83</a>
+</td><td><a href="match2-0.html#3" name="3" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_7.gif"/></a>
+</td><td><a href="match2-1.html#3" name="3" target="1">74-84</a>
+</td><td><a href="match2-1.html#3" name="3" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_7.gif"/></a>
+</td></tr></table></center></body></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match2.html b/examples/presentation/student_handins/intro_python_exam/report/match2.html
new file mode 100644
index 0000000000000000000000000000000000000000..6844d546a2463b555d45ebecd1a192955cfdfc01
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match2.html
@@ -0,0 +1,3 @@
+<html>
+<head><title>Matches for /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py and /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py</title>
+</head><frameset rows="150,*"><frameset cols="1000,*"><frame frameborder="0" name="top" src="match2-top.html"/></frameset><frameset cols="50%,50%"><frame name="0" src="match2-0.html"/><frame name="1" src="match2-1.html"/></frameset></frameset></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match3-0.html b/examples/presentation/student_handins/intro_python_exam/report/match3-0.html
new file mode 100644
index 0000000000000000000000000000000000000000..54c191e0971d8c97bc0c8412af30c56be8cfe200
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match3-0.html
@@ -0,0 +1,80 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py<p></p><pre>
+<a name="2"></a><font color="#0000FF"><a href="match3-1.html#2" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_13.gif"/></a>
+
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+</font>        assert(False)
+<a name="3"></a><font color="#00FFFF"><a href="match3-1.html#3" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_11.gif"/></a>
+
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+</font>        assert(False)
+<a name="0"></a><font color="#FF0000"><a href="match3-1.html#0" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_50.gif"/></a>
+
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+</font><a name="1"></a><font color="#00FF00"><a href="match3-1.html#1" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_15.gif"/></a>
+
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+</font>    evaluate_report_student(Exam2021())</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match3-1.html b/examples/presentation/student_handins/intro_python_exam/report/match3-1.html
new file mode 100644
index 0000000000000000000000000000000000000000..3f5ef1ea0ebb16d1426ea52c0aa66eb6f9c74bc9
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match3-1.html
@@ -0,0 +1,79 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py<p></p><pre>
+<a name="2"></a><font color="#0000FF"><a href="match3-0.html#2" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_13.gif"/></a>
+
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+</font><a name="3"></a><font color="#00FFFF"><a href="match3-0.html#3" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_11.gif"/></a>
+
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+</font><a name="0"></a><font color="#FF0000"><a href="match3-0.html#0" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_50.gif"/></a>
+
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+</font>        assert(False)
+<a name="1"></a><font color="#00FF00"><a href="match3-0.html#1" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_15.gif"/></a>
+
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+</font>    evaluate_report_student(Exam2021())</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match3-top.html b/examples/presentation/student_handins/intro_python_exam/report/match3-top.html
new file mode 100644
index 0000000000000000000000000000000000000000..c44bf1d2a4cbac8de79da017b2768fddb4d9817b
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match3-top.html
@@ -0,0 +1,21 @@
+<html>
+<head>
+<title>Top</title>
+</head><body bgcolor="white"><center><table bgcolor="#d0d0d0" border="1" cellspacing="0"><tr><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py (91%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_91.gif"/></th><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py (91%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_91.gif"/></th><th>
+</th></tr><tr><td><a href="match3-0.html#0" name="0" target="0">14-46</a>
+</td><td><a href="match3-0.html#0" name="0" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_50.gif"/></a>
+</td><td><a href="match3-1.html#0" name="0" target="1">12-44</a>
+</td><td><a href="match3-1.html#0" name="0" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_50.gif"/></a>
+</td></tr><tr><td><a href="match3-0.html#1" name="1" target="0">47-62</a>
+</td><td><a href="match3-0.html#1" name="1" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_15.gif"/></a>
+</td><td><a href="match3-1.html#1" name="1" target="1">46-61</a>
+</td><td><a href="match3-1.html#1" name="1" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_15.gif"/></a>
+</td></tr><tr><td><a href="match3-0.html#2" name="2" target="0">1-8</a>
+</td><td><a href="match3-0.html#2" name="2" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_13.gif"/></a>
+</td><td><a href="match3-1.html#2" name="2" target="1">1-8</a>
+</td><td><a href="match3-1.html#2" name="2" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_13.gif"/></a>
+</td></tr><tr><td><a href="match3-0.html#3" name="3" target="0">10-12</a>
+</td><td><a href="match3-0.html#3" name="3" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_11.gif"/></a>
+</td><td><a href="match3-1.html#3" name="3" target="1">9-11</a>
+</td><td><a href="match3-1.html#3" name="3" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_3_11.gif"/></a>
+</td></tr></table></center></body></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match3.html b/examples/presentation/student_handins/intro_python_exam/report/match3.html
new file mode 100644
index 0000000000000000000000000000000000000000..869f0041dc47c5045065ff330ba870db91743bb1
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match3.html
@@ -0,0 +1,3 @@
+<html>
+<head><title>Matches for /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py and /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py</title>
+</head><frameset rows="150,*"><frameset cols="1000,*"><frame frameborder="0" name="top" src="match3-top.html"/></frameset><frameset cols="50%,50%"><frame name="0" src="match3-0.html"/><frame name="1" src="match3-1.html"/></frameset></frameset></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match4-0.html b/examples/presentation/student_handins/intro_python_exam/report/match4-0.html
new file mode 100644
index 0000000000000000000000000000000000000000..2b96cf7fda77c620104aff3a74109e02406b8279
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match4-0.html
@@ -0,0 +1,73 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match4-1.html#0" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_80.gif"/></a>
+
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+</font>        assert(False)
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match4-1.html b/examples/presentation/student_handins/intro_python_exam/report/match4-1.html
new file mode 100644
index 0000000000000000000000000000000000000000..43a14f89806ff39676eb2d40ebcfc5751c8a78cc
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match4-1.html
@@ -0,0 +1,74 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py<p></p><pre>
+<a name="0"></a><font color="#FF0000"><a href="match4-0.html#0" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_79.gif"/></a>
+
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+</font>        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    assert(False)
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 assert(False)
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match4-top.html b/examples/presentation/student_handins/intro_python_exam/report/match4-top.html
new file mode 100644
index 0000000000000000000000000000000000000000..8083b07e58f091654e4066cef3e5e86050f60413
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match4-top.html
@@ -0,0 +1,9 @@
+<html>
+<head>
+<title>Top</title>
+</head><body bgcolor="white"><center><table bgcolor="#d0d0d0" border="1" cellspacing="0"><tr><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py (80%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_80.gif"/></th><th>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py (79%)</th><th><img align="left" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_79.gif"/></th><th>
+</th></tr><tr><td><a href="match4-0.html#0" name="0" target="0">1-44</a>
+</td><td><a href="match4-0.html#0" name="0" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_80.gif"/></a>
+</td><td><a href="match4-1.html#0" name="0" target="1">1-44</a>
+</td><td><a href="match4-1.html#0" name="0" target="1"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_79.gif"/></a>
+</td></tr></table></center></body></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match4.html b/examples/presentation/student_handins/intro_python_exam/report/match4.html
new file mode 100644
index 0000000000000000000000000000000000000000..89e25755022135e37cee22cdb21378555a2ceb3e
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match4.html
@@ -0,0 +1,3 @@
+<html>
+<head><title>Matches for /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py and /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py</title>
+</head><frameset rows="150,*"><frameset cols="1000,*"><frame frameborder="0" name="top" src="match4-top.html"/></frameset><frameset cols="50%,50%"><frame name="0" src="match4-0.html"/><frame name="1" src="match4-1.html"/></frameset></frameset></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match5-0.html b/examples/presentation/student_handins/intro_python_exam/report/match5-0.html
new file mode 100644
index 0000000000000000000000000000000000000000..42df39de4d2cfe87b2ce90c7b31ff8e2515c7e56
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match5-0.html
@@ -0,0 +1,78 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py<p></p><pre>
+<a name="1"></a><font color="#00FF00"><a href="match5-1.html#1" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_13.gif"/></a>
+
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+</font>        assert(False)
+<a name="2"></a><font color="#0000FF"><a href="match5-1.html#2" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_11.gif"/></a>
+
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+</font>        assert(False)
+<a name="0"></a><font color="#FF0000"><a href="match5-1.html#0" target="1"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_54.gif"/></a>
+
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+</font>    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match5-1.html b/examples/presentation/student_handins/intro_python_exam/report/match5-1.html
new file mode 100644
index 0000000000000000000000000000000000000000..fafecce3c1f31cba09cbb7e3b577ce843033c433
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match5-1.html
@@ -0,0 +1,78 @@
+<html>
+<head>
+<title>/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py</title>
+</head>
+<body bgcolor="white">
+<hr/>
+/home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py<p></p><pre>
+<a name="1"></a><font color="#00FF00"><a href="match5-0.html#1" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_1_13.gif"/></a>
+
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+</font><a name="2"></a><font color="#0000FF"><a href="match5-0.html#2" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_2_11.gif"/></a>
+
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+</font><a name="0"></a><font color="#FF0000"><a href="match5-0.html#0" target="0"><img align="left" alt="other" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_54.gif"/></a>
+
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+</font>    title = "Introduction to Python: Exam spring 2021"
+    assert(False)
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 assert(False)
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())</pre>
+</body>
+</html>
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match5-top.html b/examples/presentation/student_handins/intro_python_exam/report/match5-top.html
new file mode 100644
index 0000000000000000000000000000000000000000..f06250845d7691f71864aff7b00161dd83fdffed
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match5-top.html
@@ -0,0 +1,17 @@
+<html>
+<head>
+<title>Top</title>
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+</th></tr><tr><td><a href="match5-0.html#0" name="0" target="0">14-52</a>
+</td><td><a href="match5-0.html#0" name="0" target="0"><img align="left" alt="link" border="0" src="http://moss.stanford.edu/bitmaps/tm_0_54.gif"/></a>
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+</td></tr></table></center></body></html>
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/report/match5.html b/examples/presentation/student_handins/intro_python_exam/report/match5.html
new file mode 100644
index 0000000000000000000000000000000000000000..d43ab70a454e94c9c02545f14aedf5c02203a560
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/match5.html
@@ -0,0 +1,3 @@
+<html>
+<head><title>Matches for /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py and /home/tuhe/Documents/unitgrade_private/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py</title>
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new file mode 100644
index 0000000000000000000000000000000000000000..0218ebbde0a63cf859789e32a55a4ea99c40c826
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/report/report.html
@@ -0,0 +1,36 @@
+<HTML>
+<HEAD>
+<TITLE>Moss Results</TITLE>
+</HEAD>
+<BODY>
+Moss Results<p>
+Wed Oct 12 07:50:30 PDT 2022
+<p>
+Options -l python -m 10
+<HR>
+[ <A HREF="http://moss.stanford.edu/general/format.html" TARGET="_top"> How to Read the Results</A> | <A HREF="http://moss.stanford.edu/general/tips.html" TARGET="_top"> Tips</A> | <A HREF="http://moss.stanford.edu/general/faq.html"> FAQ</A> | <A HREF="mailto:moss-request@cs.stanford.edu">Contact</A> | <A HREF="http://moss.stanford.edu/general/scripts.html">Submission Scripts</A> | <A HREF="http://moss.stanford.edu/general/credits.html" TARGET="_top"> Credits</A> ]
+<HR>
+<TABLE>
+<TR><TH>File 1<TH>File 2<TH>Lines Matched
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+<TD ALIGN=right>44
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+<TD ALIGN=right>50
+</TABLE>
+<HR>
+Any errors encountered during this query are listed below.<p></BODY>
+</HTML>
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100.token b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100.token
new file mode 100644
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--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100.token
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/exam.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..262d11592a27c8839add4d488a7ea58981439980
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/exam.py
@@ -0,0 +1,63 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    assert(False)
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 assert(False)
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/exam_grade.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/exam_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..67b272c3fa4478273b3f9ea2a69d03e824a5ae17
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/exam_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/problems.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..01470fd6a8f3426c1188b72cdefbce349d30d51e
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221000/Exam2021_handin_40_of_100_0/intro_python/problems.py
@@ -0,0 +1,89 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        assert(False)
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    assert(False)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    assert(False)
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    assert(False)
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100.token b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..68797b0b56e3e0418752dee0d9f778ccfac9ae26
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100.token
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/exam.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55e4054ddc73fc4f1eb4dd703fd66c972baee46
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/exam.py
@@ -0,0 +1,63 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        assert(False)
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        assert(False)
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/exam_grade.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/exam_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..67b272c3fa4478273b3f9ea2a69d03e824a5ae17
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/exam_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/problems.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..60c2a01aa4cdb29a69db4d11a1ee68df1b7e3dc2
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221001/Exam2021_handin_80_of_100_0/intro_python/problems.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    assert(False)
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100.token b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..74ab5a3d17172fbf232bac79b3b1e04cd6252cc7
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100.token
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/exam.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8cf1b269e2829ae11a1282b4fca1be18ad49acb
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/exam.py
@@ -0,0 +1,62 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        assert(False)
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/exam_grade.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/exam_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..67b272c3fa4478273b3f9ea2a69d03e824a5ae17
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/exam_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/problems.py b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6f738567b918f4abedbb39d563de5b306008c7d
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/submissions/s221002/Exam2021_handin_60_of_100_0/intro_python/problems.py
@@ -0,0 +1,87 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    assert(False)
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    assert(False)
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..01470fd6a8f3426c1188b72cdefbce349d30d51e
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/0_problems.py
@@ -0,0 +1,89 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        assert(False)
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    assert(False)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    assert(False)
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    assert(False)
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..262d11592a27c8839add4d488a7ea58981439980
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221000/2_exam.py
@@ -0,0 +1,63 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    assert(False)
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 assert(False)
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..60c2a01aa4cdb29a69db4d11a1ee68df1b7e3dc2
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/0_problems.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    assert(False)
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55e4054ddc73fc4f1eb4dd703fd66c972baee46
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221001/2_exam.py
@@ -0,0 +1,63 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        assert(False)
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        assert(False)
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6f738567b918f4abedbb39d563de5b306008c7d
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/0_problems.py
@@ -0,0 +1,87 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    assert(False)
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    assert(False)
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8cf1b269e2829ae11a1282b4fca1be18ad49acb
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tmp/submissions/s221002/2_exam.py
@@ -0,0 +1,62 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        assert(False)
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/tokens/s221000/Exam2021_handin_40_of_100.token b/examples/presentation/student_handins/intro_python_exam/tokens/s221000/Exam2021_handin_40_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..1a0ef426f0359ec6fd12754b46385cba59ec162f
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tokens/s221000/Exam2021_handin_40_of_100.token
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/tokens/s221001/Exam2021_handin_80_of_100.token b/examples/presentation/student_handins/intro_python_exam/tokens/s221001/Exam2021_handin_80_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..68797b0b56e3e0418752dee0d9f778ccfac9ae26
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tokens/s221001/Exam2021_handin_80_of_100.token
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/tokens/s221002/Exam2021_handin_60_of_100.token b/examples/presentation/student_handins/intro_python_exam/tokens/s221002/Exam2021_handin_60_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..74ab5a3d17172fbf232bac79b3b1e04cd6252cc7
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/tokens/s221002/Exam2021_handin_60_of_100.token
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+qDpCx4kHHAAAAAOF6qMXQKjvQAAGJ1QHC2AKUaoc2scRn+wIAAAAABFla.
\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221000.txt b/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f9f4351309473a5d62f166cca66b108b5a69eef2
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221000.txt
@@ -0,0 +1,109 @@
+ _   _       _ _   _____               _      
+| | | |     (_) | |  __ \             | |     
+| | | |_ __  _| |_| |  \/_ __ __ _  __| | ___ 
+| | | | '_ \| | __| | __| '__/ _` |/ _` |/ _ \
+| |_| | | | | | |_| |_\ \ | | (_| | (_| |  __/
+ \___/|_| |_|_|\__|\____/_|  \__,_|\__,_|\___| v0.1.30.2, started: 12/10/2022 14:50:40
+
+Introduction to Python: Exam spring 2021 
+Question 1: Q1_WaterHeight
Question 1: Q1_WaterHeight:   0%|                                                        | [00:00<?]
                                                                                                    
Question 1: Q1_WaterHeight                                                                                              
+
 * q1.1) test1:   0%|                                                                    | [00:00<?]
                                                                                                    
 * q1.1) test1....................................................................................................FAILED
+
 * q1.2) test_water_height_hidden:   0%|                                                 | [00:00<?]
                                                                                                    
 * q1.2) test_water_height_hidden.................................................................................FAILED
+======================================================================
+FAIL: test1 (__main__.Q1_WaterHeight)
+test1
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1867, in test1
+  File "/home/intro_python/problems.py", line 86, in water_height
+    assert(False)
+AssertionError
+
+======================================================================
+FAIL: test_water_height_hidden (__main__.Q1_WaterHeight)
+test_water_height_hidden
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1879, in test_water_height_hidden
+  File "/home/intro_python/problems.py", line 86, in water_height
+    assert(False)
+AssertionError
+
+ * q1)   Total..................................................................................................... 0/20
+ 
+Question 2: Q2_AstronomicalSeason
Question 2: Q2_AstronomicalSeason:   0%|                                                 | [00:00<?]
                                                                                                    
Question 2: Q2_AstronomicalSeason                                                                                       
+
 * q2.1) test_seasons:   0%|                                                             | [00:00<?]
                                                                                                    
 * q2.1) test_seasons...............................................................................................PASS
+
 * q2.2) test_seasons_hidden:   0%|                                                      | [00:00<?]
                                                                                                    
 * q2.2) test_seasons_hidden........................................................................................PASS
+ * q2)   Total.................................................................................................... 20/20
+ 
+Question 3: Q3_TimeAngle
Question 3: Q3_TimeAngle:   0%|                                                          | [00:00<?]
                                                                                                    
Question 3: Q3_TimeAngle                                                                                                
+
 * q3.1) test_angle:   0%|                                                               | [00:00<?]
                                                                                                    
 * q3.1) test_angle...............................................................................................FAILED
+
 * q3.2) test_angle_extended:   0%|                                                      | [00:00<?]
                                                                                                    
 * q3.2) test_angle_extended......................................................................................FAILED
+======================================================================
+FAIL: test_angle (__main__.Q3_TimeAngle)
+test_angle
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1895, in test_angle
+  File "/home/intro_python/problems.py", line 71, in time_angle
+    assert(False)
+AssertionError
+
+======================================================================
+FAIL: test_angle_extended (__main__.Q3_TimeAngle)
+test_angle_extended
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1903, in test_angle_extended
+  File "/home/intro_python/problems.py", line 71, in time_angle
+    assert(False)
+AssertionError
+
+ * q3)   Total..................................................................................................... 0/20
+ 
+Question 4: Q4_TicTacToe
Question 4: Q4_TicTacToe:   0%|                                                          | [00:00<?]
                                                                                                    
Question 4: Q4_TicTacToe                                                                                                
+
 * q4.1) test_tic_tac:   0%|                                                             | [00:00<?]
                                                                                                    
 * q4.1) test_tic_tac.............................................................................................FAILED
+
 * q4.2) test_tic_tac_hidden:   0%|                                                      | [00:00<?]
                                                                                                    
 * q4.2) test_tic_tac_hidden......................................................................................FAILED
+======================================================================
+FAIL: test_tic_tac (__main__.Q4_TicTacToe)
+test_tic_tac
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1910, in test_tic_tac
+  File "/home/intro_python/problems.py", line 50, in tictactoe
+    assert(False)
+AssertionError
+
+======================================================================
+FAIL: test_tic_tac_hidden (__main__.Q4_TicTacToe)
+test_tic_tac_hidden
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1928, in test_tic_tac_hidden
+  File "/home/intro_python/problems.py", line 50, in tictactoe
+    assert(False)
+AssertionError
+
+ * q4)   Total..................................................................................................... 0/20
+ 
+Question 5: Q5_StandardizeAddress
Question 5: Q5_StandardizeAddress:   0%|                                                 | [00:00<?]
                                                                                                    
Question 5: Q5_StandardizeAddress                                                                                       
+
 * q5.1) test_standardize_address:   0%|                                                 | [00:00<?]
                                                                                                    
 * q5.1) test_standardize_address...................................................................................PASS
+
 * q5.2) test_standardize_address_hidden:   0%|                                          | [00:00<?]
                                                                                                    
 * q5.2) test_standardize_address_hidden............................................................................PASS
+ * q5)   Total.................................................................................................... 20/20
+ 
+Total points at 14:50:40 (0 minutes, 0 seconds)...................................................................40/100
+
+Including files in upload...
+path.: _NamespacePath(['/home/intro_python'])
+ * intro_python
+> Testing token file integrity...
+Done!
+ 
+To get credit for your results, please upload the single unmodified file: 
+> /home/intro_python/Exam2021_handin_40_of_100.token
diff --git a/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221001.txt b/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221001.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8da72f29015d31aaa8c528e5ce677e40c85796ab
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221001.txt
@@ -0,0 +1,65 @@
+ _   _       _ _   _____               _      
+| | | |     (_) | |  __ \             | |     
+| | | |_ __  _| |_| |  \/_ __ __ _  __| | ___ 
+| | | | '_ \| | __| | __| '__/ _` |/ _` |/ _ \
+| |_| | | | | | |_| |_\ \ | | (_| | (_| |  __/
+ \___/|_| |_|_|\__|\____/_|  \__,_|\__,_|\___| v0.1.30.2, started: 12/10/2022 14:50:41
+
+Introduction to Python: Exam spring 2021 
+Question 1: Q1_WaterHeight
Question 1: Q1_WaterHeight:   0%|                                                        | [00:00<?]
                                                                                                    
Question 1: Q1_WaterHeight                                                                                              
+
 * q1.1) test1:   0%|                                                                    | [00:00<?]
                                                                                                    
 * q1.1) test1......................................................................................................PASS
+
 * q1.2) test_water_height_hidden:   0%|                                                 | [00:00<?]
                                                                                                    
 * q1.2) test_water_height_hidden...................................................................................PASS
+ * q1)   Total.................................................................................................... 20/20
+ 
+Question 2: Q2_AstronomicalSeason
Question 2: Q2_AstronomicalSeason:   0%|                                                 | [00:00<?]
                                                                                                    
Question 2: Q2_AstronomicalSeason                                                                                       
+
 * q2.1) test_seasons:   0%|                                                             | [00:00<?]
                                                                                                    
 * q2.1) test_seasons...............................................................................................PASS
+
 * q2.2) test_seasons_hidden:   0%|                                                      | [00:00<?]
                                                                                                    
 * q2.2) test_seasons_hidden........................................................................................PASS
+ * q2)   Total.................................................................................................... 20/20
+ 
+Question 3: Q3_TimeAngle
Question 3: Q3_TimeAngle:   0%|                                                          | [00:00<?]
                                                                                                    
Question 3: Q3_TimeAngle                                                                                                
+
 * q3.1) test_angle:   0%|                                                               | [00:00<?]
                                                                                                    
 * q3.1) test_angle.................................................................................................PASS
+
 * q3.2) test_angle_extended:   0%|                                                      | [00:00<?]
                                                                                                    
 * q3.2) test_angle_extended........................................................................................PASS
+ * q3)   Total.................................................................................................... 20/20
+ 
+Question 4: Q4_TicTacToe
Question 4: Q4_TicTacToe:   0%|                                                          | [00:00<?]
                                                                                                    
Question 4: Q4_TicTacToe                                                                                                
+
 * q4.1) test_tic_tac:   0%|                                                             | [00:00<?]
                                                                                                    
 * q4.1) test_tic_tac...............................................................................................PASS
+
 * q4.2) test_tic_tac_hidden:   0%|                                                      | [00:00<?]
                                                                                                    
 * q4.2) test_tic_tac_hidden........................................................................................PASS
+ * q4)   Total.................................................................................................... 20/20
+ 
+Question 5: Q5_StandardizeAddress
Question 5: Q5_StandardizeAddress:   0%|                                                 | [00:00<?]
                                                                                                    
Question 5: Q5_StandardizeAddress                                                                                       
+
 * q5.1) test_standardize_address:   0%|                                                 | [00:00<?]
                                                                                                    
 * q5.1) test_standardize_address.................................................................................FAILED
+
 * q5.2) test_standardize_address_hidden:   0%|                                          | [00:00<?]
                                                                                                    
 * q5.2) test_standardize_address_hidden..........................................................................FAILED
+======================================================================
+FAIL: test_standardize_address (__main__.Q5_StandardizeAddress)
+test_standardize_address
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1933, in test_standardize_address
+  File "/home/intro_python/problems.py", line 31, in standardize_address
+    assert(False)
+AssertionError
+
+======================================================================
+FAIL: test_standardize_address_hidden (__main__.Q5_StandardizeAddress)
+test_standardize_address_hidden
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1941, in test_standardize_address_hidden
+  File "/home/intro_python/problems.py", line 31, in standardize_address
+    assert(False)
+AssertionError
+
+ * q5)   Total..................................................................................................... 0/20
+ 
+Total points at 14:50:41 (0 minutes, 0 seconds)...................................................................80/100
+
+Including files in upload...
+path.: _NamespacePath(['/home/intro_python'])
+ * intro_python
+> Testing token file integrity...
+Done!
+ 
+To get credit for your results, please upload the single unmodified file: 
+> /home/intro_python/Exam2021_handin_80_of_100.token
diff --git a/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221002.txt b/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221002.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b6689e9b6631ea43b55e298a6a4958f03fc1d155
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/verified_tokens/logs/s221002.txt
@@ -0,0 +1,87 @@
+ _   _       _ _   _____               _      
+| | | |     (_) | |  __ \             | |     
+| | | |_ __  _| |_| |  \/_ __ __ _  __| | ___ 
+| | | | '_ \| | __| | __| '__/ _` |/ _` |/ _ \
+| |_| | | | | | |_| |_\ \ | | (_| | (_| |  __/
+ \___/|_| |_|_|\__|\____/_|  \__,_|\__,_|\___| v0.1.30.2, started: 12/10/2022 14:50:38
+
+Introduction to Python: Exam spring 2021 
+Question 1: Q1_WaterHeight
Question 1: Q1_WaterHeight:   0%|                                                        | [00:00<?]
                                                                                                    
Question 1: Q1_WaterHeight                                                                                              
+
 * q1.1) test1:   0%|                                                                    | [00:00<?]
                                                                                                    
 * q1.1) test1......................................................................................................PASS
+
 * q1.2) test_water_height_hidden:   0%|                                                 | [00:00<?]
                                                                                                    
 * q1.2) test_water_height_hidden...................................................................................PASS
+ * q1)   Total.................................................................................................... 20/20
+ 
+Question 2: Q2_AstronomicalSeason
Question 2: Q2_AstronomicalSeason:   0%|                                                 | [00:00<?]
                                                                                                    
Question 2: Q2_AstronomicalSeason                                                                                       
+
 * q2.1) test_seasons:   0%|                                                             | [00:00<?]
                                                                                                    
 * q2.1) test_seasons...............................................................................................PASS
+
 * q2.2) test_seasons_hidden:   0%|                                                      | [00:00<?]
                                                                                                    
 * q2.2) test_seasons_hidden........................................................................................PASS
+ * q2)   Total.................................................................................................... 20/20
+ 
+Question 3: Q3_TimeAngle
Question 3: Q3_TimeAngle:   0%|                                                          | [00:00<?]
                                                                                                    
Question 3: Q3_TimeAngle                                                                                                
+
 * q3.1) test_angle:   0%|                                                               | [00:00<?]
                                                                                                    
 * q3.1) test_angle...............................................................................................FAILED
+
 * q3.2) test_angle_extended:   0%|                                                      | [00:00<?]
                                                                                                    
 * q3.2) test_angle_extended......................................................................................FAILED
+======================================================================
+FAIL: test_angle (__main__.Q3_TimeAngle)
+test_angle
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1895, in test_angle
+  File "/home/intro_python/problems.py", line 73, in time_angle
+    assert(False)
+AssertionError
+
+======================================================================
+FAIL: test_angle_extended (__main__.Q3_TimeAngle)
+test_angle_extended
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1903, in test_angle_extended
+  File "/home/intro_python/problems.py", line 73, in time_angle
+    assert(False)
+AssertionError
+
+ * q3)   Total..................................................................................................... 0/20
+ 
+Question 4: Q4_TicTacToe
Question 4: Q4_TicTacToe:   0%|                                                          | [00:00<?]
                                                                                                    
Question 4: Q4_TicTacToe                                                                                                
+
 * q4.1) test_tic_tac:   0%|                                                             | [00:00<?]
                                                                                                    
 * q4.1) test_tic_tac.............................................................................................FAILED
+
 * q4.2) test_tic_tac_hidden:   0%|                                                      | [00:00<?]
                                                                                                    
 * q4.2) test_tic_tac_hidden......................................................................................FAILED
+======================================================================
+FAIL: test_tic_tac (__main__.Q4_TicTacToe)
+test_tic_tac
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1910, in test_tic_tac
+  File "/home/intro_python/problems.py", line 50, in tictactoe
+    assert(False)
+AssertionError
+
+======================================================================
+FAIL: test_tic_tac_hidden (__main__.Q4_TicTacToe)
+test_tic_tac_hidden
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "<string>", line 1036, in _callTestMethod
+  File "<string>", line 1928, in test_tic_tac_hidden
+  File "/home/intro_python/problems.py", line 50, in tictactoe
+    assert(False)
+AssertionError
+
+ * q4)   Total..................................................................................................... 0/20
+ 
+Question 5: Q5_StandardizeAddress
Question 5: Q5_StandardizeAddress:   0%|                                                 | [00:00<?]
                                                                                                    
Question 5: Q5_StandardizeAddress                                                                                       
+
 * q5.1) test_standardize_address:   0%|                                                 | [00:00<?]
                                                                                                    
 * q5.1) test_standardize_address...................................................................................PASS
+
 * q5.2) test_standardize_address_hidden:   0%|                                          | [00:00<?]
                                                                                                    
 * q5.2) test_standardize_address_hidden............................................................................PASS
+ * q5)   Total.................................................................................................... 20/20
+ 
+Total points at 14:50:38 (0 minutes, 0 seconds)...................................................................60/100
+
+Including files in upload...
+path.: _NamespacePath(['/home/intro_python'])
+ * intro_python
+> Testing token file integrity...
+Done!
+ 
+To get credit for your results, please upload the single unmodified file: 
+> /home/intro_python/Exam2021_handin_60_of_100.token
diff --git a/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221000/Exam2021_handin_40_of_100.token b/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221000/Exam2021_handin_40_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..5299c6053a962053214e5c413eb07cf9e9592588
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221000/Exam2021_handin_40_of_100.token
@@ -0,0 +1,395 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221001/Exam2021_handin_80_of_100.token b/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221001/Exam2021_handin_80_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..c87bb157271e542dc8f868dbda320e8d100af525
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221001/Exam2021_handin_80_of_100.token
@@ -0,0 +1,395 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221002/Exam2021_handin_60_of_100.token b/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221002/Exam2021_handin_60_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..7d111a647d4a40c54f7c966dee1494e01db2218f
--- /dev/null
+++ b/examples/presentation/student_handins/intro_python_exam/verified_tokens/s221002/Exam2021_handin_60_of_100.token
@@ -0,0 +1,395 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/process_handins.py b/examples/presentation/student_handins/process_handins.py
new file mode 100644
index 0000000000000000000000000000000000000000..019059a1eccb41d3ff7e8d4452c11facb55011d3
--- /dev/null
+++ b/examples/presentation/student_handins/process_handins.py
@@ -0,0 +1,6 @@
+import os
+from unitgrade_private.pipelines.dtulearn import process_by_zip_file
+
+if __name__ == "__main__":
+    process_by_zip_file("intro_python_exam.zip", instructor_grade_script=os.path.dirname(__file__) + "/../instructor/intro_python/exam_complete_grade.py")
+
diff --git a/examples/presentation/student_handins/process_handins.py.pclprof b/examples/presentation/student_handins/process_handins.py.pclprof
new file mode 100644
index 0000000000000000000000000000000000000000..a8bd43f8e083a88e0c58a353f31ba17973350e96
--- /dev/null
+++ b/examples/presentation/student_handins/process_handins.py.pclprof
@@ -0,0 +1 @@
+{"profiledFunctions": [{"file": "/usr/local/lib/python3.10/dist-packages/coursebox/core/info.py", "lineNo": 18, "functionName": "xlsx_to_dicts", "profiledLines": []}, {"file": "/usr/local/lib/python3.10/dist-packages/coursebox/core/info.py", "lineNo": 248, "functionName": "class_information", "profiledLines": []}], "unit": 1e-06}
\ No newline at end of file
diff --git a/examples/presentation/student_handins/token_evaluations.pkl b/examples/presentation/student_handins/token_evaluations.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..535fa63544d8c05156bc0fc556529afb87de2aeb
Binary files /dev/null and b/examples/presentation/student_handins/token_evaluations.pkl differ
diff --git a/examples/presentation/student_handins/token_evaluations.xlsx b/examples/presentation/student_handins/token_evaluations.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..332ec899580c546cbe43b1fe5c1f1a08ef8c1807
Binary files /dev/null and b/examples/presentation/student_handins/token_evaluations.xlsx differ
diff --git a/examples/presentation/student_handins/unitgrade-docker/Dockerfile b/examples/presentation/student_handins/unitgrade-docker/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..0ba4b77a284c02a8d364d63d1da9505e09ffc63c
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/Dockerfile
@@ -0,0 +1,19 @@
+# syntax=docker/dockerfile:1
+
+FROM python:3.8-slim-buster
+
+RUN apt-get -y update
+RUN apt-get -y install git
+
+WORKDIR /home
+
+# Remember to include requirements.
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Not required.
+# RUN pip install git+https://git@gitlab.compute.dtu.dk/tuhe/unitgrade.git
+
+COPY . .
+
+ADD . /home
diff --git a/examples/presentation/student_handins/unitgrade-docker/requirements.txt b/examples/presentation/student_handins/unitgrade-docker/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb32e1584ee5c69345f21bda8b1135bd342f9009
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/requirements.txt
@@ -0,0 +1,9 @@
+numpy
+tqdm
+jinja2
+tabulate
+pyfiglet
+colorama
+importnb
+unitgrade # Perhaps just this and not the other.
+requests # For unitgrade, may remove later.
diff --git a/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/Exam2021_handin_80_of_100.token b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/Exam2021_handin_80_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..c87bb157271e542dc8f868dbda320e8d100af525
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/Exam2021_handin_80_of_100.token
@@ -0,0 +1,395 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam.py b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55e4054ddc73fc4f1eb4dd703fd66c972baee46
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam.py
@@ -0,0 +1,63 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        assert(False)
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        assert(False)
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam_complete_grade.py b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..60e63ee51618d8d5412a9f69875a70b883f82068
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam_complete_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam_grade.py b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..67b272c3fa4478273b3f9ea2a69d03e824a5ae17
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/exam_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('QlpoOTFBWSZTWb+CjKMAa8H/gH/2xVZ7/////+///v////5ge973vHvPPp8rw5H0Ck97a++z51pQC7aFC+hiIH0PQPQeoIuwXnue9nvW2nnvrw9bbz5Drb2uMa1lvWUCjkAeg+nva23318etZ769wn3zvJPj5bmD6z3Xoc8AK8vbBvb3yL3nemHbkr77ydWx9CbmtLW3wNKH3XW66jrz2U+Pffe7ztuve7rd10Yh3t0vrKi9059nuHu47RVK98enPW51dRvfAK9N8wFs+fZ0c8nuffd4p1d67zj7hyemAj73Fud27q10apadj7hvb1dbhKpr11t7b3lgwmM+6bHaq7vfNtlPb2NzU+xo9fd6DkbPuaFd73fRXw+vnDbc58LjL7z1dKhKT3evn0XsPvsGd58jO1L7zj161vvaYHlXobWrunmeu9nnu+89U+2t9g6XMMlvc058rHewlNEEaAQAgTRDAg1Jsk8TUTepNqepkyHkjNEPUaNGJ6TQSmgIgkJoIEyMRo00UH6p5EYmmRptQGmgAyAAACQSIgiZSnpk9UB7RT2qep4oaeSBkyAe1QANGgaAD1DIBJpJCEAE0SbEaSfpTaj1MmjaSeKNqe1I9R6m1A9Q00ABoG1GgiSIQAjTEamIGgJoyamFNk0npE/UanknlPTINEGRpppk9TQSEhCBMVP00ACAnqVP2ij2kmyo9I9TNDUDaEfqgM1BkyA0cFf1kV9B8jSgiez2JSRIp74KquCjAgsCElJPuqLFVF+i/2X58LqoaY/p/0tE/9mRH9Lz/jt97n2bOT6f8Z/YPCDg/D6b4cduM4Of+kEileHCeZaEyQmTen88b7r/epF73HXWs6im7tS8JhFqIdoVR6wZLqbg8lOc7keHnKiBRn9X/Poru9wwuXdCW0T7YyefRwlQpRaxGofFEkOQJydxOZzy6QfuPuJTj7f8M8QXJ2fXjEREbrf4ypslTNWXdkhgVfdsNiKCpuIKIoPNQE9rEWQJBJEkCEGQVkWQQD8ZCxFkQRX9KTIAAGf9WFhIAMEgQFgEhtsyJ1m+MkBvX67zXo3sZdO3nvth3JwPSlqQdjNguuMCKA22hiFWWRoqoKKf3GFgxFQFUgqCAiqwFy0kf6vUf/O7m9gZ4dJ+r1i9nef1f9Z5ccNdYchGoyLGDKiPHedrPRK+H1JDxOCTXbM5PP3d3s9vgsVJAcbIR94KWJve23JxdO7MwvsBgWe/WYwinbyTn43saLo+9+GeXOb6883FGNlaLZ7s5MwYytNntbecovl6dx/5MypcKjN1MNfxXGzC2hn9Wkfz4/6fx1n149ffLeIlGv+Ur8sZh1bVUb9D5JH/yTBL9OkfrKYdb0cN+OUoTHcj3J+n0ez0e+S/c7egRiMxjY7F7nz074PjCcSY/yrpK+fuu4+9efOmLSEYLAkDlwh0fH2wYzlW6P88Igan26t2rR2P5ZMlA+6Gxs8+Yy61hYPX4DVbfs0WDsk0+41og2QG0mlTlEPT4Okz4wf1XrIypjMlP64kOQzuk7HSRETicHOWCvpMXtMvq6cYyMdxlJo/L6fOX7NR2k1Xm3oefvhnmz9mBMvT8a6+n3VIv/db07tJ+UvSRQc8qGRIm1uQ8kgTqI+HOVbnvXP9ljnxbn1iMBe6PK6ry1De22LQeqAzP0QUs89HDXItFSjlbtqiU8Mims2+zJ/nLEQuHw7rz4V4Ya6lNuA/vx3QT0cnKN2r/38iLtJ3zUg3CXLljrjfGt0p175cMp0pc92LUsPhx0lwrKTZY8VG8nrLLPbhLLQoR48J6YPNF6JXRu6PBnTnTCm6Za7b1ydE8fK4rNT8aKK7p0IWMTiXJyW8cO8lw4FCaNS96db0U35cZ3k79IJXaGFZl+mHTAJlWHtDQxne6LYQRojlaUKyRaIxvlFyF5vHeHGFVJpdynJpn+O29uJ6h+l5+5/EttKE7vVUiyS4+Zalyar5TxTE+aI7nkXJjcskyl6RWlyBepo+Q+Yj83pHbUm/r3kAiKRpG1ikMUF7xSJ4PEEGEBZgJ1yOl92pLGPJhqjjbNSBycRKG0vsz59kwgEnfbcLRxYJUL8GZ/Q0A5yiJEbe4nYb9u+wIr28ZcC43MxcCjFGRMjYsY2JvZ1XY3FJhvME7fDyOQxi3xw5k8EMSbkghIUDbesmeS2s5F+eIHBERFUE5NB/Cnc+Zem2/bMgctPY9tWDPVZsdCWO8IcXtrSZa+bRMpgP1oP1x6kcuq6paRiyIeeBxhuiPw+Grh0VCHAxOEE49e6pufci9KINz2TdSzaDNSIzvAtTIqZkC+kIz4esx8qjg+3NV4tzpSFRvRuxrhs91agfTtXfyqzzZuPJHLZhBsgQ/RAzldkHPFZj+LqR+2cIJd2qwmaBZx9rgg+qrnykcbiRrd8ZkhExfuBx9pGGLE2cfRtiYlNT3YQE5M/+ZzeCpyIpDEh9pYN2JphqjssJBcDYvC8/Fe+YxxOzXJc2xmMs7J9Kqz5yPfaDfTbGyT5CvbFPup6HtGbNvy3tzyqfwy/j2KA/KpyqgFT8ikJakE1Wmtxz6EE4HqIxK7XGO7c+lz4DrPALjExMCeEtbiWnstI91ts8qUsrGgmuhMYDkLrqHXTaRNbXmeE7cJk7PFXbSHPjwz6m9xC8dz3T8M/PX4iMTQfRokuCz0yk5tgp0bsSKRp9lLpOIUxOTztEJpnJOcOs4uMgsdlXadCrnXEnMym/61eeyn1Fq5Eh+m8IJSuJP16xnKud4bUDPftnkSTPKdhHAw+Zy4hpdco5H/XEcrLjKOGVNnkCw3U0kb9sypK1z3w5dhL14u3514Fv7bZtPLnuFcCOni22SMY2GLF+43NTm0/zaf1qCFf7R28zd9Y8CRgWG3G0smbSa6BBncxIjBB4F1QzRvm0qtyjKOJZxN4nItGBGl+Clm0H5EzysXXM7QdHdhM15eMGUT0KVKXls8898yvp3xqXlhezGNlhV9uYY73GD9sJtVYjRocsDyyjkWVxu01nwKWy9ioNZw5ZIO3xvxrx6Ju0Wcyn19B0ONUnHaHMGWfTefccOUTBUoryxqqNN5cjTbpug/d9TwbNMlxY4/4SyDr1H8DjKcqNG/fXQkJCv4aRbS+jTZsUPdJNUF2LtOvVtTWatxMi2Ne0ZlSJ03WyLxzX6cjVGWKSAEJCDNNXZXlqSZFKl75Yrm1kEq4T6eNoKduVbpDhyMa/LPY18XS4bFQs6vZcj6pMZI1ff4debskIwkof8osuz/lnVqM7tTlOZITNQPDwVtOKiYs45Dn42kzLjYSRV98pmiPbFlct08iDVfCJJutK0JKZGWL6zJsWOA+4t5wcrszb0amXVrnFvjH6ZEymtxT7ZSKeG1vOSNDWa3zPzNgUPv79szUc1DQXxNCeV3PfI0LNemWuEjHfv+8s9pNGIqVFSbrhBTrOWl1cbyHavYhptqEjqU1Qb4zZ09IfGdj51oPI8d99XwpmYmaJp8W+x05P0AZvpAc9uu0GMsOptPI12+xbbF+85z3zK1bnDwQHjltSShQDgkmXEEntB0EtiPbRX0I+rZ78vTujcG2HWm8vnW0HPd3uaNbsbXBOaLPd9Or2M58NrUO6B/z/brad35Pz3yLW42m+xMOor+a3RLNvLnA3+Bafztmc5+Y9DeHmuBDPuuTx67a61mbvbjqV1f4PK+7e+sdxkaNqKRm/jnhrdOVdG2rRn+yaZFCRMNUZmU3jedcDEq9yrLW8z0p2kW913Xf35JG1rag/VXzORwIk3ZkzZxoXu30WcYvS+8x/2un8YGc/D4CN01XMRp6VN8mV+ryNav5LMdez08OGu+mzg2d4tfHWAzdJ2I9q7eJoscqj9wmy7LIYdy2/HdUL2VckjiEIHQCFTTsFM9DoXOHFNuXirkRfQbe2094Gy757Z9+Oni+ccjEqb8+GbNLWZ4v4FdYoqFSkpwEp8hycybu26szet9VZ7EVxhpeGVMBxmTUKHgGR9Xe7Yc7ew92qCj6HA6IXc+qCDWakh0hSgTAiGJUjWXMfoSym2E6nHAtahULw1vxEwWrYrYsSW+HMleKN1c7UyESCHpdwcvlgThjUH6EfsVU0qRUvNTA1sx9tIp8tJREQbZR0OkwQVQk7uyUcPXU0V4lu4b4CtAmcKxKdozXrCaG73C8ICfIzHXjvirNvxNCTZbmHs05FpTLwzQXxobGJcEIVbHtri2CwIFdyloSKUzD4lGtSmEZtMhm1N99ByZrnab3Q1knol1oxLkqm6k2Z0rwiUgulhfHVCGo16FBfg6KwugoMGhiuh2rimlZQ7EiZ1STNOhhMfDWhfYbN8PYZ2aWJF0l3anxQQYDi00nO/hQdKjg6fnhtkssRW39xvNbgSdkby8eWhgG+T2k1d3A3GhdtrAmvKtKqluHUyb4GN7bQa6lM9+dxlPLtI24ZhYML7FdYkJg4I+ky9pXUuoby0JOJ05CLQjEUDQ8ogeSk3vecmk4Le7Xo0ESTI5EqkiQpIvl2cgUeUVt1G5uEqJB4Iton4yzsqsGsJorijdphVOmjrdHBXKCI2m9NKYQXEGtO9pGKxyDmrhFwrIEiedVNyk35Fb5a478G/IX2J37OcjR6HbKpnfnFYcezyobqEI0d2ME7HfUOUCyO1t06SEb7r9b25TOFr+8yxGu1s2BI36Y5E5137ZbQtZhkYIL6UDNlNkwyC244YwO6nE9Cb+xYTpzgJY65EkIXxu1INEIR7PWeDEWmuIkWinUx0J2NYi/ALhGFx7HKYP0qM+AfvfJxO1HDnsF7Ybdz4F2xHZFx71wmnxw3Tzr3c+tfLPZxo1oo3Xl+bN5XI/YKAx+OmrLrHB04TBRrKNnHLUZTtvz8HfurdeM8V8LduTYXCad9DxVfoylAl5xB6z6NhepEyGtvd8K8Fxk5nsiiEMOCruTKRrhFtWYOjG30Lhmqnr0iDyWPPt8tmm3rJJ7d5fji4CZoJU8aE0QYscCprv1D8e8kQ6K9yJA71sOkGpaUHVFwTYk77nqXFIRSaOBvwV90Px1euGUYYtPI37NdBl7ynGDGd9bYS8k17TJuaFTVSnsjW2N5sy+2TUy9wjBz1fWMr9X6J2e3xM8emzXQydN2ZXPaTuem6ARTRvPMuLrPlc19cyYaztqmGmirT79pdDS9s7CZ9+st0j1qd7sLLyhzp2NruXCyLXQszBzEylRrpZZddYvvo9FJsnfTasf4O2lJZy3yRKwn3Gu5i9827HnQuxegcZZY4+LQbCrdiaF8ifE5FQmIQEBRnMXFhXiKGvZH0nE9xkfM5M7jePf8DNjOTAceMvwXBU82G/N9sjC71V3+HlXbhuiKJsPae/RwIayinTXeDPw+RFcYxlzMFkfdDnrGe7Lbsz7zU6aPJPYdWzCMv6f8Q+Vqo0+BoO/RuqCm9WI75a+G5aMVoNilPntnMKWyEaYxDaWd7DxXBAu+Jx4+n4J6s+3uu9p2vt2WFXlI7Xcj1lXWSeRbY/KPfdTL/o9Z4N9du+PbPJY67bSnLfv7+EskeCi6mFFfL+HhfrSapv+j02jK12V/M02xlO+1TgdHRdHjrla1mlnp9mend78ZzxGvbp4nVpkLleVeUGvU+QMh+Zr33NlLzZA5Bs2JbrnfXoO/WRdc+znLzjSn0eZPGXHQNuJQg7P/FP5NN9vr7/D5sDTbLLDXHeomkafRuhhRfOnTHxrlpzffjxLnZqO+T34HcJYukOHJ4Xj0UZCm0TQJOeW678/eU2kKkRCWlDnklaSbxMGyMeRJyJH738MSuewql4ODRC2zJFUpZh07bKn7jPv5e5D0T0cZ6lsvOvuUXUOUq/v68t23zvgWVy6ft4fnkeO808NM9EzujoQoeXlApYUdBjhPSWIY0seFg05ogGCaSjokSqOMdEwT/V4QU/zofQC7G0iheKxfE629NY39yIxvN1LNyWamvluae9RjWNtNveqip1/X5lwenSjn3YRmtD0J3dTeNRV39Qmg/Veglax1DGFUjJVr66zcxF26dk7p3Sg4tUf6Ymi0kqTibjUXIj8mXyIiEhHZ3qdgbLgwa/bSUH1Ae4o953/zOJYwKT7TEpfZ4fJ5Pw/pQ8mv2kw93yXxVN8AlzfQlkMbQKVq/BuFkhbMj5W9TQ5fufDRjGqqisSHehDp0580r1b7qieQyiAsVVWSbcWAcIccbYhnd4OYGhQ/dVFIKH3oyFeisKzG2lRtlRtjEKoklVA6nGYwYgFYjCv9lphmVIbDOT63UtcA5iO5U0dQtIyfJ+wHBik0wNu+gbAwNmSIqKRGSaHtti2Ts1jeOWZGnnFfGhSRdqG3UqRRf9XDm9t7wf08dsTuU8vt6uI6xkmjmWRxTIRqMjYowct5HpommRY84nVDZ2ykeG2PH5WH68eFbp72QPXbqpUzFcPHVxS4rILMfMd7TltTlSv37llnrH7iHRYhYjdqZd+K8skqX3kSuh0lKbvXwaZ97FBcYFVeSHaD7MqnP9geqks7nIv0NNCpAjWgFGQQi652YTI/3R+Y8EUbTC79a44L3P4yJPAjvLw4HHxqt3vxZaElbx0Trmq44s9z7tp+XYIo98zfSGRU8eP4i2ai3OEXiG+aPMx5dY52ZK0pCxeh05HsCxDvMhxDu7cS6fRq9j73i959BkHFHFtI6JOG0ebkE40EoM5qHELtzq4NuRGOMXntcBh4pM4riH4bHbf9lro7dIur/CX+eLN+57M+6K74H9XRtwqxsbeBqX1tcYkFSXIRiGDIXr3Oy+UuJ3OB6OQwqGMUo+xz9GwuDgQ9BRTgh4svSrOeFZF5DRHgu8toNmYzeaHcada2bNJE3OiemHlBRNTqohE3zUZyd27jFeKHFcm0z285Ey9OsNesqxZw1VgxasWV0bbLzxBY4TGeeFjl1teciaua1Q5EaiUE/jpz6+BOVCv5Jjz+k49NcGNRWdKywcjjI7wuvBA+i6WSeigRB8pFfKL59tv33RI6bb7PuHJZHh94fw/y/p1J6d55eKLt3/xJzHNY8cE/GTktaxg79F8vtcrl6PVIjNeURGKUaLlpmqJyQ5BXxicxThR+peF0rvwfq92K1l2m6YX+onVIPmzfitsfCO1I2lflOI/eaJcZV7qlyr342h5mN7NzJPxUy+KwNkxi8v2w+LXWzpqZPd28OZu28eqVG82RoMM+YjNaOt28PRKxP0LouWM8R+/v+now5LRl17rt7Yk+CIeeiTRfwk8o2Lwua+JRCOhELmX6HNKLRiZViAnJW8pPm80Yr2q9MWm6QqOD6Y8st+GuWNsbTeeFHANyywWdPi3IlZuiKBYt8OHRfGSjihDcEPAsQ0tb58r+OkzRyQ1yPo50uXGFDo0mTaJuxfnyvNcZCoUueIFNHlf6qyPmT9X37PfjETwH5h+YNxypXyVRPcUxzVKZfff1TIuind1qp8KbGc2cHTbG2FQVmVXamta418cvk7INSCB3Ijo3Nc47+c64Obct6f5LuMVvMRLp8hl3qdDhdpc29NbAndfgzIIM3SthJF38vo6VpavCWi106ZydEpZO903/bGBzuousnS9T3Vkkqub+dDjnS6Srn87xNZMez1n4tTfZSwTvuVtHzU/Q+Xjj+i35fLlKhRKfslBEpwOXZWlWfGjl3ld358J0wTePGO0jw0Z1yXrtGBum6N1TZj1rKlay5JxXS6ybbxwIL+/4YGE6E88JXb+e1ZJXaVdQ8Vl28nkbnyq7pZYSLLGlFnfTipYLouvoppKvbLPd405ld9TFQtimEOSqoraIe12s/O++WHyGtqYc7cIv3U9m1fZKptJ2BxbezGJnhOKzeFOKwNjLnxkeauEVXKqq7ogT+2MJep8IvjDWRWil1zn1uendIVi6fsRzNPF64qKPxY0kKMr6JtEs5i839MEzrKkSi2Mo9Ly8noop6pbZK2r98ZwTQKKc6499Tzpmi5KUO6QkEV7++Ch7erc+U7ZtGzg0EuB9ZIG+n33VNocOojdUayEhTYZ0Z98MDaJmsJDaMxiGxjA7t164+eKu37ixAxzt17/r8EGiF1WGfr2Ja2WHGBq5RpNLs4Ve2GLGfsz44lsPXGO0vRLGdwp1HIF7/KTWndWT5yJJJj25dGuWIm4S/X4ecohoeIy7Q+L2zgylPW3/fdethy7/F5S437zn3N8EXJ+OFWbkrSiu3h5bLDCfBYX74adHm2SZYR1RE99QJS31KdxE+AU+bALtxpXQp0vu+e1+s3xayOOhSEZXXc1K6D6NbiVcpKIZYEmacp+z37jdTW7pDpMsE6daogwePKrFZB2JXQl3Eb5ak3mDjsLXl4ccSaMs3gTPcd2vjQwvfRc7oO0vfF+NP588s7yvmledL05uI022ujhNuIO5he+VR2NZ1MwlpnDzUiIOBc2eNZ15Z5yJd94Zt0z2rRvpeFVhk6a46aQwmxJ67TKmD8esM8qU2ZmZTzVNeTHxzwUxeMCtNIuikBiY2hScRt2uxXh41Mbhzltv5UQZ0axLOZ5WnR/n2uLx1woeLYXl9HH5ZGMp338PT2Vd0+115jpl0+w70Fb/VueAwwIbeI8EYmW6KYzg4XbZLLuZu++61sSKFKkijtWhWHsbQ0IIHfHzffewbsqtPhkQHbpdeSE/cUaTcuqPHCRejarhZDQizd2l28m91OO3hRtR09Vnl6k9TEZH99Gs9lwo0lEBr+Jk+u6dWL5obq7oMRcqNb7sT4VbmiDfj7jqWA69LrguR40GyMPMvuONwYCHpkd37yxMnPBWq8RgQOOJ3emj7K+snPsq81pCod5XgjmF+eGkqiClH7kEXLpdC19FNt4r1pWdGkwK1NTHttv5LUZHL6pm8MULevcfE6Pzq0ez6kfB/SOhQl/U5+SYZnuO3HUNwjggXJioToFlF0rewytLldLmBudcrk7dvTO6XmT9umqPn2SYuIfUmZ3hcmXPEbXfBo4Tb51l2+HnLC1DBpZenKMcB8fPThKfPO+XyTSTI96dhISSSBQ6DWx7BfLYjCq/yxJ8utpXSvYvUtDIM3Rraz7vPENGlK1GpFv38cRsNWawk6ziQGVpR5kEYM1Jo5ag3m0RaaW5EibJ0XcxwZoph4+M4OADO8nKeQMKZPixzi8kwXyGhO4M18TVFCER4XK1GNtVBfFNd8xhgGBmoNJll/Vy8NplsUIfsCA7EeLtKO3VkXHEKGqIMDaTtZJyMnI/FC5qy0OciHROE6H5nYoSENy7xPQVQxoRzMIIG4rViQaUMj9PN4wDFhAh56ytydNZuk2Q3ZHtu2Y69ivMr3MpMQfcyU9rA0HtO4LyDebcVQSQPPnM4d2OIx31wdYaUFwQnTpxkCEubS2RqwgqhJj2TqyvkUcCRusOWSkgdDrzKDjH9RwanBhB4OXYBa68O0k0XU3A2UpJL5q6+6+jwm0D0DHzh49JNaYwznd1Voy1Q8uzZRsY/bjMw1zX6A3x9O9nS5PwY/w5Nm576QJCFhcHu6yLI6kZUmYIuvoQENHcyuOreRc7u/tq+BOc/Q1pkwQtnaOx59c33BEHQnaMOQ0ThrQg+g0OJ4gbHA79QnefEY/ixDxPDcAoBwfc1cCgWJBxgaQPhPva8me/T4ha8uCDsG4IDUPMtxDAZvytyX7DnL7fX6qn+9ecqfclcEKJO82LfZeglLb63bG02ZrJkhmQ24+Un5nPQ+PK7a1KIPuzlbYLvJAmmCasA7EJyjA1NWqCjEYiMVR3pG2gyzzTScdl/2xhgyjFeEENeESOWJMYk3oj1UAtdhogctKVFuUphkYHv0Yhx4w3uME/NprHIoDdq+0tM8A+6MiNji8J50muNoWanQBqDx6j0FZ+v8nheo9zRxpTgYK1J3nwe7C0eRWs9xNp4ufU48fn1id6kVrxVTkTWuH1V49qR4RtQ8XJBuOOSdVRttr0pelKwmxYfXSjTMbCCFd12eNAizQo1TJJpS8MWwZri1uc+jvt153SzlWGXQ7i8hkZTTkJDJvtJswXGfUVjuDUYw/018hbdvCPjMrjOIN/0/nWYW1Q83VMk5FbdM9dPwuGXnVBZ8roOxITR9Xl/pJZBZDAF3fbm6EeWnsOyuHeVLed6yQZR7MZp8xnge88P2iGPp9JApVmx1cOQ1daImh/x51yTNtff1X3NrSBLnjj5+5K6hzH4qdxSNcXcQ66c1b5iNKtU3OxozhzcPV253xrV11rjQczyQ0c0CkNiFk40sRBFjPAaCoiMc0FzFhaoiUG0LFKl5lCwEQ4DgIYTJgXfHJuhKNOCGzkTm6waKlFbDzkl5aZ+Gzt1DQ2cIG7r7eb9tnVe04L2psGwXjG425RPZb+UhzyONzUtO6yvFakxnoWYwaWp5JJbJz5A87WTW7ciUrSfXYho0RtSVKQ9ZSlv0dab0x4lzalLgOHWxUwcILiMh1WPwqkyqfRqsta0sGQmZ3SSZNkXZeOJO+nmNZodkJD3UylSWXJLC0xOh5hUmqpJnKyioJqnqEXN7u1NSfPbzmWa27mVO+FN2aWkhMJS8qoqCsnRdWkbWBBjg7hSB4mMLx0MrbHqKEg2oMb3zurphum5EbYiEaTOJJmSZp3xV642/GVFxnFRcZOqnEp0Fe87ROdHWCxk85zUySZycpXKinhSFtz6AuhzCrFq4aMY6sktZeGoxnloVlcz6l5lNUZ2SQkkXqmIOMcFKljFGJN5QqupA05ApJtgVKpiiGJg2gx0NUQUNNRRSCiybQY1hYBknUQ2xZPChLIRtYlSGAUTPY89hvNRhpPZebU3OjpDidW55+HuHlPBWHelQ72E4E6xNtBVRFRYpw3saPi0VjwGi9p3azYFAUUQSOrK8klVJKIwZBOqyVIiwSW2CMBm1ow4S76gWGQp3dvZczu7zy353ZPnlDRJ1mxrIos26dNKREMxthdFr4RPAs9ZJNGG+98pWzzxJRwi4Vh2LODliiaAgk2TF18pxW3bcq1kZjkMHAqh6332RHOn53tjcaNBZvgIjfTNHHLy3CSpOtm9c1TrUM8PUy0FzBENowlNXZcovWYKdKsNXopIITPLGkXcES8dQ15qpKuMDZfemN0993Og53wxPtOiMfB2SCOuJ6URTUu5gvve9+O4ioyfi8kLipY71++89eDNvmjnrpjXn5xoIl+DfgQ7HdBlpUXDiaY2/MnnbDsaWqUsczxnD8cBsfkvx06Sai5KiG677yery+IvdPO6HWh2ZNvVC26dZ2c64Y2xaJpmcszliWEPf8xxxfl4tgwQF+Nfz7KlU9U+iR5nRMwbPHjfCBrJlEESOxkww7DMaSbUh7RTQsjVqNLTCw0FsEMWJ1CYE8PxOAgs3vm1BE843numaC203S1VwGCKU8DF1xVipzSnDrsJAu2XjANFGI1mESqO1OAwjBQ6OG83vlJMNrucnPEiajtiDBEX2JWoKiy9+2HrvO/hrpjwJimOOu+U37I0zLWMbGGGBnc2gVbehhIozBQRFSZhxgM2u7vkGiw4ZpamUueCLyhhMmzzJ9Vfst+dcTzLHim1GMQOmbwZ0oTEj5gzq0YzFDmuoTbU4g7UoaiUtah0RS79m11UOwc2mHlG+G2RzA3LCExOiExvvc1bPsfyK8Sw/PIrNFaAzlb4kSiDUHWkHM5LY2MLYOxuh7JTVLl2nTW4xoI672x/dQBbr9sMafWwzGhNi5sLyBGbN8bJuIBRBJUioy1O1u2iTGdbCw1JuBpwPCfd6bwFU1LHoC9iTIziuWJYmWbqhyEVy6JCKSWGLxwguzprWYmbRNepK9+Gl3cCybETJCzo0mK7BIGxL5rXQNXRSa7bDK6aMxSRuXexMrW24uhTLblimAkCRiCZEBzpEMw2nJhfZ6gz/pX2pI+L/yR9+Euv3Pej+/8H/JfiqST1X2x98WKfhA2PeiWCkSo8N9Duw2A58EQkd44vjDH4n6fm+Uv+6G8NxL7JYcOMWbPSGP4xQIakSg0clN3En3Ee3dfCp/mH+gZ/oH1wchPKZ9T+mO+L0zGI84KmLa0rvoMECeMDuZ35Z99jszjZpMyo14tAacQVURX39iZ1azKaaA8WBUPuJDplVcbybWva6ysU4eW+g+fXafPvt1o7Z327FyhAOkY8aqCX4uUzThLOcGSWxOCiHEOk9vCEY0ttT3RPNRpW1v5r9CCaHexOtP+yHSnbT2ysi6dfmlEpfTKnWUYilxfWQ6ndWvrUvwqbbX0xnvufOc4lZBKHU4rIyJnI8f5Efd/QDju9yOVTBc540+ov9/9GKEWEfx0i1kWSVX+YpYH+x0aoENAaLCKBAUArIGkUIVAWQFg42ACwkKgoYwMZFBSEMSQP1MgGh1qt2DITEkWAfOatge73tqw1vvjs6MKLXCl1zAlWeeisNGDbwPHL03q7DSuEzWCvp1GGy7SNbx8ltIeScTo9SImb44juIp8h6LcinlrqRRJEv9y9C8wy9D6jWjIVSi40OIh5Jjftqd5cKZeJlv4/Jz+suc5eOG6ExhhWlYKsTrkE/0mgbZ3F3LEzPLp6t9ybIaqwUgg221ntOJejTKV9y88GxC90FkTkbXErkox/kqYthvDVcQSvBs+WLSr0+8jVCQ3NnB9buyqjmMocl3jvreRgS8VkPMMJNoi+MFMdpIjzDvANiHL+0mlh/UaGmmMhfU02duvb1LaT2bM12+eqz16P50mjfnNTPWhd/ufn/9nx/G/3P+v6j7N+h1dBRVFFFB8yjgaCzzTmV22PI+ahzROSpkJ/F+UQYNi/OYDN30ZeQPENUOsQDSf3NpcAbbNeVh51iF9WhsYpPq/1GnjHe5OewaU+j1fKTZaqqBJJJ6MdwmajsCO9KChs+w7dx46KPEe77HfYPwSXD92Umq1wdbWP4NNmYnsQOEGykSwBNJCIgyEqx7rhgUqlCeI+Q4lmiiv3S4cnaW/aH/jWp56Nmf1hLs3B27iYUIR75BEIFzxPVJBQeBw8gbRfJptMbPQ4lFHtnmqJ1gHPeOHH2O3+Aem+BmP2ZzqNJSy6ZyE6w8x5X1JgmShMoSaRIZiij0LjDj51OIccQXzpJCIQgwiEBA0WoeR59+Brw1ifd2fM+frS1FHdffsOU+16vXPQMCeIaL+2J8VSKqq+GjmfL5/ITKjzc4+HUb74Y8gjtYbIfunXedIJb0bCu5h0wwLMT0enyhHZvDc7jQfKaXyRKhyAhcOt8xayQN/rsWzsjPP6l1OB8MNhuPAyPsN2O3Xwn8n2v8DoYshkYhauwz2jCXEudL84ZIGLGF+mOqmgiFSx/EwhRmvQLgmx++r1nebGwqn4Av3DIkkST5k+H0Eouh+DlqqqzNwnFAe4opLohPAIo95EtxCqt6EpGD8dhY0vbt/KOzi6siF+NG1uUQ3qmBsa+GTN80/uZBEyf5pE/fb/G6dX+9Mqwp/vxcm1RYbK8de7GmJxX6Fy4g7OPpEMHj0BJOQp4H2Bl6rs/aa9nJFVn7t4aNK2JlSKH8cotIu9R1bizRSie9J/7TwPardX/T5Yz7sZP9bbchJYN7+lm4YU/y000tXhii5tCQfL8eOpLveuUk73dJRHT67pm+nY4w59ksGR+yQ7f0YM3ZAd36Hy7HX189oJYYolvZ+/EMjFxP6B1Efi4YcSnu3abU3Tsf10HRv4x1FxMyHEtDOePEQeoMN4xEGvk0YwQPJDlMLqbHxwNYDP2/tKWnd+BmAL5uKQhqzKnPr+AYGUQGIHA/YB4rvg+Cc5L0rsnJqHhMb0Fip88pL+l5fzJqXkrvl8pE4zW/04cGkV2SizsVJN2AYzO2lGKBAJKPnftaPocfalfg/T1Vn5UZOAtvz9t3eqm17Ze6ceHLHVnn82jV16QcbiPSO4exc6mJhUnRK/ki7k79MT9TSSDpFoen9LuF+RZ4cnvU1JHL3J1EUXaXGZGXnFjaUoahKB9+kwa098VE6hiDuILwcpfppzOEOL7HLUJO4l6bgJduNeCZb3e/+XjV7MnqHSEQrc4U2mpNcPCM8OI1dnRKb4wF6iYh0X7R4SeLVlJXI91oiTQrPAk35HeROs97tPx/ZG8zxOUnUDv0K4Hs3ET9z9LU+jx5IkrtQGlx28aXV9Y8jq2WyIbEEeKaEwkJkwpSz7SM868kS/4pwvvpLNeNMhaVm0LIfaLU7VQ58rmLEPLPS7hnabjnzqGyYFcmomkw/0WRXNxnw1kur8tTV5OMJ8qXtyXrT8UlTvj5VeUOrH6To37PkIkFQ+4SxQuFahftfk47KoSUGoI/UeUU88ok/pp86704uucWN74e3biKy+q+WGXOMXtX8/M3nppV4wjZ1k+HrD05GNpuJvle4Swrp20dNr7fy3Ht5F5JEZH6WvKrViG3Z33BcNRrTHfqWnd+y/hZN6I2J/juXJXRB9mNyiU3Tpx+mzwo4ReiS9CGcWMlGk749b5ZvnszvhdZT39Vdo5naQtlQwY/xJBsfY3VnN6aqJGfc9WQ6MmkUYmy2i8r2jq3aK5QVPysR7z7uCt11mlE+HL6cfk25DJkszt1rnvZke+lIIxZjUXgcwqL0aiKqHaWbjQjqh0jHSMbDzXC3q0xrQJw+Vw4ko7Es7ilZPLf1D19n70FgojeDm9KKKUEJIo1OY89TwiRUvIPFBciwpN70noSm7Ou9Y47T2n65/hPWckkeOMXvOZ8VVEkb0zis90CP+D6W0xlSHb2o7Zy75BREkLCI+zfjBrXW66dEFFnSeGPcJ652sG81ZR9J6GrrftvJ25dC/G4d2Blg9k9bYToeXNz2kK8+VDfqXC7OB0LAiMtp+lSS+Dy6YGvLzrR5y2XttGKwwue2B1staDIyL1YRh2SdT31pyMriyeXZAVNU0jixGrz05uz5V3NpVUz7FUTU/W/qoj+K/oVE6+uMW17Yel9moq6nqJ8d19FWQDsrez2Z+HDop1ucrt6KVuuKYNfNpHOZdyXjDHf8ko84e5VO56YSifox/Vne856jmm7awUoxFDhr4zX1fHc3o9tdHn11l6KoHZ5HeB2cTnrG/qr1PTGuHC/i6TcV+r7qtiiRjHMg9fM4bVi7B0ivbug/uv32lW/u59Ol23TpcSN88IhD7oo8YrpRqhVMg3eDtB3uqsfPXqvItYTwntOkoewijiZVniZmKXn7P/Pz2S8QcYmuMkiH+c5n53UfQfnzvGYPqPKdx2mBpeTAdsd7CSSBrhPhXq+Fr9dsMhD4S/nKC5gwbNs0k0DSFcUqXFuLGNY0tMPg+UIHYgMfJPUlTPze5oYDAZAHVIjJG839T0FtRtESP5H2DEwgQAzwPbLAi4SCfc34heMN67AVG4Ev0XtNAtLYClimKIDSaWyLdrRqoiI0nQiUD5lwdBgaOhkmTl6zIOxEOmopnzpIpITWOcO0PzDt1Jm6zEdPiMcU5mEjJAkhq1UNRXIPtAstMbS2nqccBkI8hNag+i2eg2+YGMKWtCA38IXKLZ9B/r0XtifAvcXNvhhiOjeQIENakIQjsD6q0iR1D9Ltz0w6Bjgao6uqJcDWOC7sHN54sI6hytI0G0mJGDIdnZQbDZm8cw9ZkCXIQJmILrXa7OWRJkFUyEIzc6+U9S4DIUKH8vQks6HSnaGcutVFPahWMC2kX1Ug57TKlCGtMoQRJKC56HjQw7w8ioZhWpVjqnwYagCGQQwghWGIDsNRvDAcSIvENxAPQQAsIbnQtB8wiDAa9sWzw6BKIoMiOAQ2wybtp2bZr7xpb+xnig2LfhZ5psnRmbeQtUcYof8yJQ8vPPub1F2rO3pTY2MWL36mOcbLk7w2InMFIzJKHlkCjzsTPAkTiOKciI5cPXJhGqmjxONRtldjIwQVgo02jAURPoA9oHB7OoTGa8DUIag0jAajDh3HbQ3opkUq36gcNw2E6WpPXYFWCcHfYAe9DJoL9RRvtMDdRRh9MPwhvzYjwuSBr5IG1P6QyaHIcTrTmo9JIJ4eBhnoVhTuSB8Z2m+CxD+UyDzaUCqKO8T8sIREkQSERkVXgE0XfqKMQikUhEUsZz1frfW3juglG1fevuzGCDf6PF9Vn3dB7DcQJR/JVjdVxLAsUixiChDq49aqAP0TvgE2N2ERQYgJE8IbT1nJC/UcAggiaZV2SKqwKgIDGFpViF/ZsG08SKogIKqLD/WWE5Z76lBKopA6skgU0HCwawDYtV8EbdxDSr1Gzu/6yUUeVKIqD5CAMZo6NGUWjYUraRRYfGefvt+JRnG9gPw16fe9pm4wrN4mF4PYXHrEUTUlQu2W5Y7DrTrSILcb9IC0dKcSp3ipkKNnpDeB2z8hTQgSiCxSRYjGIQlKFCwOOITtlFBkYxntNDYz2TcDrxVfuIEIycYp5IpzdHUJMyENhI4OyJby9aE3em704YJA2/eJtSigDQlnkCSzC296U0onWhrUvAXWQ849x39yipKWHUE0nUUO47dH8giCeZ4h7j9aoCB72XaTdirGD5CbgD7fD9ZT+gNfJ2EGjF6g3Cn9gQU6BiEQkQawHS7ipYViM5oTeG8nZ0RSHM47g8RpD2ggFmCWZGGMLyxoXBV993nmY7QjB5iFm1VKlBEYUkQmIhyfywpRviDtPEiRgkhGIEgBFiCQAz9knrrVvDYhjFX7IwwPn/Ht0mqSget8WYIsA+AU3MN8aBcKLHmYV6bugQwyt2Fuwo9FQfA+L/Rf1UEmiWctsCA0FD2aDJCkMWDGNhBC6vDVe1B3F50Fpgz88DPA0MgO4XmxtAIbEflvSItQbd2yN1tttbsEuWrDULi3MXqLIhWZDEhkeVVxoD0DnEt1/h2lESvpC54tnPSRJUAvtD84Rdih7oD12kAqomzDEEFemHq9Il2PC7lOFJq5/wDpvrKorYaoj+Eemdis9H3FZ4CrBikMFjDr2XPw/Si//eev/IlmvAry/STRG1cswiOqlaV08d7f587hkHMVdLLv9hUp5y++SP8qOH3Se6MdDZRBoUk/bgXh6jn81jBkIHJKwZ2vkSgE+9JmiuBLgEXUQ3dv+VcMAd/q1h844IaZCaaGkkkE2FrFhkGAGZPXrs+FevdE0mfrDvEMxkHz+FfFuPeOxRPIbDsMoE0gTIDGuiKYO4qEfJn8DOI1riIqTCGexz9vnbfoktD5rxb8B+wQfx/b/tZhfFHqxSH6hl2v1MSaabosFcTP7eBxKOI+iBqYHp/Iz0Gvmf3XMCwP/tlRcqyUEYAqbWBiExkj80gNBEBnLmognWYajNBrJhDhlBSG9RikfSF7Tfk8lqqLNyJuy9vpr9EAx/darSHpQM75w6m0HgwbFUaID2tSRKhpQUAIUsbUvIQGLpiQkJAG/FM5VA6NvydR5W7A2GlOqRkuHA1oqFL7LIfC6enO45tu16w6bllzSEkhHXQlMSERgRAZYRxsH98M27gr++JQtm7o96fHfiFIEcKMvAUh1o+BVANtgw/ehRO+qJNfGKk+c+ULnLX89gY2yb1G8hdpEoFm9nQCoDUvKYENXgQzqAw7lF96uM4GA3fsMgSiA4j88sQjFjgTo7KuRLmKocj++1A3XpD/YLh6bn/j41oFhtJSGwKkNKRqAB7F6BZNJT8PRSED20SIjGm0MWSBAoGFAvY+EuLdG/gFqhcwCoZkzM1QPWWNxpFO8rfAeaYdexGRr1RWQlI2FNnB4bWlJJSpMJrC5GmDE1HHC7pmmjilWnCLIFZvcRJAbRjLDTomNGUgrkLXAGWpEenxpDcBrQ1rKMEJfl4qb2gDw5lFNN2DTiMcCQHCMY5XrFG3uuv55AwleZkNdKuNKfdjV60HhRlr5YpOKjo6mismPkfT7xosEJ/H9P4j4yQ+6D90V+uAQhbAfrgBndO2twGqhJRSFE3cAxTEuQpSYPC4byBgLWfZ9FNl2BmAZrQRkGbEXVOJFgYoBQ6D4JcHyaX29tjwj6t1sdEfNi5iJZgElUeYPNojQ17Brp8+LAc+bIPToYFB+AUpyHleuEOjnxKLYfF0ozMpRg5EPPKAkMTQIjUMBmANkiVhjgGAhBQhjQafa4NNHwdlm7HFJI5fN4pjOFKyo5q4o4Y5FUYrlbVG2VG0kci+Ng/Qz7MiHfuza2EDeUXA+G47xpRiGYDuBcHgHRR4jnzPzlzqebXTyr9sylprEgeBHQ/ENUW4kjbXZpc1wgWuwId4feGs6VzOajLGI/Z5yh9xAPFNISdahau+FD3Z1O7UmSuo+4IGP/N/dvNKa+3jY5ELh3hVEgECxGQ2kWjr7LGcj41VI+QmYcKNg5zxEOFNErwmAuCMpSeW7w+0wME/IJ69g5lgczQJmZpMEiAwaVhWKlGMvmz8Dv6xOW0fZabx+pVuSsb784JtMFCmHOEZDEuob+AZFiCpTUcxy6jDk+cJpIRbrMMOo5x7MyJQYiEcacaTT0kgQtrpo4N1xqljJ8I6jlhssX3RDeI+kUAuDTddDjiKL5zOOPpfCx3He/33Z9s9Nyjhb2/aOkcJk8c9HTl9I6EHC0qjoHMbiW0DoazQkmhWgVDiNbg/Ygz+HAjj2OPWV4rqR/MKF7En7nT9+ZpxWxTi8sfvzN4Vo2/RIYiBJNw7ifTmY3BuMOu93ybM2eaaKv1IGMGv3RoZIuKXJCaCmNdEZV2hiwZ2JSEUOIZg5yuJ8XoV3BxL2cokh1dnbIiSGxyeYFcCQ3BrpaMRwaMhatcIalpSqEWcjNmUeJ4gwRD09eDnvinbTlmD6F6Lg4gkOkIL0LEOw7ZHFhIo1TnnnicomQQHhjG9Sc8qD4OsNVLVm031KzztnrJCTEXs0YChYNb1ysX0OzrT5mlQ8qzRntdtpbxgQkqJqmDwy119R0nbrkOYc2GOWGbKdCe7u4bXXUNiXNM+G4IMvRBIYVBhJSa3fLpkFEhVr0krH8uc1XdkFvSEzLlSNPNEumqI3dNzla+hPrePqYIJQ56pmeAfhMv1HPWrvXE27Duh0MiHdMilUukmiZFspKLS6jJ1z33NpUcvPFc974USOjB30ow0iGi63OZUIWnIfn5ubbaxNS4g5jzTDiFHnERxCvqfKeXB7DTvljkD0ohtIhik+VXIVNwHD0YxnWiVALaN7jHOzpchbqkyD2VLQ9ya68SjcPrj8GR+ijlNeUXQ4q+TRPfg10q3BxNyYtgzkgQooTelMs8weflxcXYxbWCGpmj2nBY2gVDegWaEwgR0HVUmTAagpusBzxoNh9/8HxDED9QfXZoEsveJey/ymFBrIJ1yp9MFjLxqNDESFUZrmmcZzgcB4QlSiNEOTla75eeYk4eE2Zrn53FwW7vLVGLJl3ceScVNz8UmlGJrSW3H5HKRDy+jmD4JsXXzEHUGk7v1cEOCIg7eOmKIUJ0bRmOBsSa2tWldF/QsXs3ixkOGIcgwwmDiM0QK5nyRjBMtvqaME2QhWPSEfibxpHpl6h9UELnl6R6KtpelyuMSKJvh4jVcRxdQQU+32dSQo29D1D6LVxvI43jR6OPku6Sb+/JtEYzITIB0xCCEIBIuTDELgnQ5kpKDONwMk1QYESjY5DgDqcGgIMBISAG4IDXmdBco4DoupyCMBaRgcmIFAwVAoaUoRB7yZh3ILYuoxhEuQZ0EsKDPpCgvaC+OFSLEcIpnz4/0zY3E14v3lMhlYLmtXIM5/QEMD6CB1gReo1OxUxDrJDsYHPGDFEgiKDE4Mm/7MEKMISTMzdkAc8B0yYx3RtUS7QXJ+Y083ZbqyPWHzG84DGbHcW0CC0FwTDshCEoaWKbs0iS9AoL2kORSj4uJHSsoBgzI5dhxEjI5yvDIDgQNgYzdQdzcxYm5HcaDAB60nJOROU6ycsFJ2TwKew/YrZOA8qVc5BPjf+awp14bJrFFG/xM2Z6t5p1GwghsCDnQmQn64jIL8TQDpA+n6uRnj3x1GkjGAcQgjh4noe0vZunXFCQduWCZzIrPIBkmepmQzTPfd4UHBpWFBGElEKkIUgVEWuKTOfKenFQyhP4tQ/VKlcKMwY5h7P+XnBZqdih5FhmOrh9SUFHCdBhJQXLd8ALi5WKFqBolgzzPYDo7JgKaFGInEM/WxCW43kxCyQUOwiosggUYbBAuLWL3BhLIaMuQ/DWeHuv044ngUdsVTmQLVvqVSnWqOEQIlUaBDOYB1CFA5YKHxlCIOto1i45tt+tAGGbaA4MCGIK1BVGQ+ckIqokxiIwCBBQE2IAdGLNzBDDVqOnAQmKJdgaEo/VkbMi46SAGkh3FFQ2DaHfgU9ZRImMhpcuifi/NXRL81sFKFisS3zO7qKC82kOsD/mMCHaSQ8Y+xoCUP6e/47U+73yfb3k3ZrVSh7UFkk7gKEYO5+sDX2khQrtud74Z8ZvxMrTOyOCEGQkbt26UrZUORT/S5PG23PzRQjT7BtmDsxhYtoW8qccfM43eIpSC1ttuBBnV8vZt1HTjs0aMb2KOx9qwIxzGC0QQzxTCKSLCEggQyGI1YT5YrsCgpFYiRYrJECCRR7vTViJrBQx1LZCwXaGrp5lkiQjEYIGGZ/QQ6SEJ4f1WCyJiUEIkFDyjVm42R0gGYAiEondneSGHvEwDW2HjMIf5hFiAIRibQL4RnKj93e3PaQA6BQOmAkZiGAfEsB8205Q/jIFEOyPDzabOZ4Q/biDI7wLvAh70Vb1hkCfPI+Afzf4zorB8CzYfOTdMUD4CQKRKN9MKKGotdqp50WLgWSkO5XT/gUgyIPwPL1x5ZW9HrPkA+Y7IeyLoQS+A0wZSiWCMVtWj0tDGwQqEaQmssnaY3TIaQgqMEStGQCqISoFLKAolsKKDCAkYiKiRZba2Sh/0cLVhQYgiiYWkxmDDEsYU+fb0NsxcBFZ+JzPtMv4hs8A+Q6S4dnRkm7AmFV7MIGySTxQCQxhARAA6tjeTIAHzX3tw+z835wWfrRCwQYBKhSD+VKSljJCQPvDMoZGlqjVwLO+fhDgdQBUAYeD9lMCJ5ZENKVyHxfgngl9gugfHAhJGARTKB4gPqDSV9tfPo8U8exf4LM3CSuMM4AfL0+4pjTnXrsahkJPZ023Uafaa3NFpmpobpXNXMINHYYYXKDBhwhdqPsDJAw2ESIxnNpwamGklEESM/Od3do3J5niZz/pGxirEVkSLoTmTcOrPHKMeUGdbOSdR5WUVr+dPp/x7Gd1cbmofz/FNs2znJ8fM3Y3GkMNm7WZJjEU82t64JQ2MKG924pI8+vSt38826lauihQ4ZUW5nuKHqjebD9UTe8L3vQwnDCVkeZzhwHPwMZ11qYP0/jPMeBtHzMhC1FeqVC1UgbCCURMKSoExkhSMQGKKiHcMD4n3jhQfmGINsKHBVQg4k0loOTTzLCFoMGPRj17LMO2SBxjgLiEEjFFhRd/Qo2zxr8pz1S7twMB7xGraFJc2i6zaYmiXxuImJKFRJSiFZjD5em5ueJDyOcm/XNhSXFBkLqE6AuLVW2hBFAT4Db8OMp9oqeKFKgwEgxJ3Z2hSQnYbGuZ1oQIIyKkIEL0iabGbi9++zaFs1r3X0xg60FmFYBUtowSSxJF84abiCAe7leA15jio0gYQi7d1yLSO7NhA10ZTk44NZSAz1spz+xD+dgfjQMXYnMmE6iDIpCbMKiqBY1iKA1YgKIyQWFQowiJK0YtBQXrCKtMUA0aP16R/swSQkhBNBY9JgbANAaI84diDA4rGAZiiWUtYMmtC8Q12w2HrO4L3XNwnVHQl8AbHPnPRYMknh7Pb8a6niTeX+jfjCeMOuIiiKIIpGMRFGIyLBHsNeWJGQJtqkkMAn2szwqDBVs5QfKu81ms0Fi9Bz1JlnLn4dFJ9xHiYVuyV09JaDPeDD2e96ewRqquflIX0QrWpIo40bvZXAuU94QLnWd6/lW19HxEf41zwIkC5EfGdfPj4sD13tIISIXOso+bVsOSGsHPi0HzOIcPvemXD2yjy9K9w/YzJWKlSnKem/nMKVrCNQm/XJY8GRttzj15w8WF1zxrMbb1tcVGWKE+bjgWuXlNWIjg27N85a3CR73q5xCN7nRl9yG9BHOCDsRGEToSAcVO+LcOiUUmYSe55lU9J7LsljEniATmKeZgbjpA+I2DyfMOGA0vNc76r5w8RqE94ayKlg98xRP7SIlQV8ItiIgGENIRV3eMXPMgxsRJAkV6fA7A3YCgekgP556vnxKAxmw+Y9SXF+gwCQo74XHmE8JLaoaIsbrgoSBJFGovgihYmhbTXwzA0W8ED6jU6kwkPwF8UIKQGAeiIhISBIq/PEOaQvDzHubQEkFmQLuMTQxB8vdzH5nkHkVqGB1rQEbzVgyg9GwMSUUcsr39USjUo5xM0RkAKUFqD7C+rOe1/A6uEUTDhPQdD1XoT5YPYXsYxK17kvrDUs0tqGGcTnHpyGv1+seAGKn1eOkiVBcVxpIvkWChDLxLPHHlwEuRf9qOGEDXiEfNKiWjqAgZn6Q1ZyLAiQLlKJO4akgLOZaS2hC2n4S5m0WM/WWgaQToFsm+2xPQSSipPCe4JvPOL0vdGuCLPk0XcJ3oSIJFYKIoxYiqqgRZGMUggkAYDBFBkCBJBGJx1nxICfzp/uQaHse2w5i3NIYABwQD5dJAKFOYIawE3wIpGCwUjJEWEBjAVkYwYsioxYkDCux9q9rzR7iEJS5AdI6D9PYJdfT5g8xIUV7flgZCbHIYIz+BKOgTBu8myT1QvexTAuQTezqVpN9KftxHthSHlew+T04HAD8sqqJNGpB60B+wiHrg9G3o4cG6bQFOQHn0HsN2pbhJaghlAiMxCmmpBYH5HqzNiQhFbkj9Px4bKOUifYUBxUdphScwo3+XP29+JKYsodoEAkwbNSoKKmBzTDpEKUteaSErWFlAM/kwdkkRM4UBmQtGEDc0fYd8EofHsKwFWMY/UUpnWB4Btn/aYaLm0PoPEXEbmDp10Fm7sA/rUvZLl+j2+p9FViPqhWWT7q/VrxklFjtwOSOmkR6siOS6ocqcaESoZELEohsgYiF+JRUPkixpkdoLucKFik45nVuaXuumuChtRWlqB/InQWZ2iceFXRsuXeDOWRwPS9nwlxOey+Fk6Bgl5U6taJ1CUDQg5I2xvZzcO93tP9r4DqH6NAMkl4EPYwYMF0eOe5HkGKOQmHwjqeH5eSJCAQIxjGSRIMCIhAinLkY/+vtv7u/68f8oHdjhe70OsDYWOkgItuKO3AdLTaww7yyLC5dTrxp4C1HZJeMEbRoqy0q1R9jNjcs0bSwiw4yob1b9UtYcO6hhdsoA7UK1gtNjWQxdio1qIokohFKqU2aS2g5SVqhvaikc4mAkspuIsSw1kipRGg0tktSol5ct8d2owMimIBFKFgpig4BY6/2liBDcbDKGfw9G7UmT2xX4TuitocwgaSMDpSbopCAbAi1DWu8dRgvSYFhRR3nuLkCHS0Y6MV0idB4qJDx/MV8hcIF0pCfk78QwdbImfgzheKTmQ9SfDLO2SHj7TyJRYOk6OERRYCnoDDSDqhDxG7xLkYQoyeZt2fp1FM5BXUwKEwpV0lBZXOl+k40FjNYXmd/aYDm9baLZI5B90zPoD0Sz4u5FtRyEg5EA3/xcfte7YMezVoWGUJRtWgJtbI+Vgg7X2CQICQUYgER69cQSMAo0ZWRbIlYoNKBkrtG1jN0cus2FXhWqHiL0XKXJM9CTO0g71RJEY9yDZvXkOxl3VfXe+7fYgpdPnBM8rXXv3lDDoczc5BosRQWQdimrhBstLqCq8J3HchPAoVEdJYLAh2Z9TAw/EfAV7NrjdBObRWTGIhmn9okCn3Lw/OlSA3ok4fKlkxDOFPp1usL3gmal83tN5Y3nXU9VCfB2E8YfJPIZFh6fKGu8Re9LES20RCiWpFYyCJRBQaSKErCKRGJILKjayVsEgRDjzyU9zNe/pDoAQTCc1GDAaQxZGjFhhkbD3Qq8QW4zptZeByLU4VkBNeYa5KTqZw76zMvEzhv3iNEkcBWSzwQes2kCzLDa8DtKXFnjIXLVxeikfG2Qd8XibCA7cgjWCNLQif0RAE2kKIvi6oAKBcSypmTm479nuZt+D4N5UlGVqs995ZXjMJIbiBYHeRaIjtaeneNLvNcN/9jDDAzEcMYgMp4wzE+DtISl6ZXZzqYqCCC+g1nJk3Ng1myhsQ1hSaGRqhEhS+DKAQSmHtz8yIMWNbXQV/rEOXYnqRUw+tUrqEgMb4OrzDIARQW8xLONVNpYaWgwZgMIlpoatoQIQggZQqqKowiTbM06RWtrKNRSPRNHApHRJQirQrKAmaghENQ0rrYZ4o4KTJEyMG4hN3IAiUqYJzbMTdoOoFQowMGAYwVAZCpRBMzTM0oopZrMEaiheu6LrF6HivIMMycano5Jg+zOOJgzTOqatUFDIlEMHbcpijNasCKdUBf3D2sGh0nNPgWgs+RNHAJOXVMJgQk3STE6kkoxUvE0xULXoUS8RvbpKv5zApJAbJsFsXQDAzCpUGKiCIgEImmDFyxycr7Y9R+9eRYqgQgEmSJm4QCVzjo/GhjcwyQrpipJIRkjzuUPgnVbajuUwLeFToD0TDVDmgVZ4AlxTxF22fAcZFkAxhToKRKMaBLsEuxuXG7FIQqMbookzIDEUTgQWMqDaBVGAxFDYsClMaMlImIDECIsVBQUikFAWRYIIBEjFANywIJal1g0K0CmtuBwIONxiENEkc4kgQxCRVAkOcDlYHtMFoGCbfd8ub/FR470EZggOwOuDIRhm+ncFf5EV6WiADstsUJfdSdPPZ24H+JCPkXdqJAAk1MGMEmE2HLiER8ZoHh5UfSOhniyitgVBsGpJ7BgjIAKCPraz3oee3nvMNfw7BeFZgCboLjUfu1tgvp3SyeJD3ns8Z7QRPTDA9RYUWFQWAXNy7mrDajYBRFKT9+KhYQsBGBIhAUYBAgCACyQRIsBGEWQtESUNDQQQQRlIUKUloWkyM9hrUwsONWcOhgscpKxRjEBBEjIb2d/g9Quac4CqE9YYOCLgbJWbyhiA8yjr6jh65jJE+dCbfNcS28kv6bIIxG87rfWcfaNDgUEEoCEMUyPyHPxm6X5b59JHASMs9ET0TQ5Omg0Nl0gD95GLEZ8UKbsNJxTW1Ssjpr2vzH2W7SFe40KD7LV6u1JmmFRQauC62EkYGxHBuddrqGAH0vKJIAGR2fMHOlCiMhIwlU0pAupoKVeBz65NKGdAeRrU6xM6o/yfvRfZ9R0aSMFB3KQYSQhCMCMEdJ+AXOgunVieNgYPTKwXWi2OgvYLhRkSJEPfc4BZV+yIJ5R1BBU4e1IYRDAQZhcSS0LCIVClVHKQUWEEZFRBSTIIiLAC0HQdZVyJAYGARRxED+pXFyoSMrJpULRBbVQl0NDY98KDITNBHpx0w6CCURC6bS78J28DqDIxDadhY1o3kVPDoS0GlGiyvdTkgLAWC2uiERzhApOT7UhjDMC1CcICiIayhEZGDJjLbNIXSgK7Ukoj0SUUPGzaanJJBycEONBET9xhR0OjjJ1CiWAMw1J/D1oGIYpkEGgmJqDNJFkFOCCdhEEdpAECgIIhfywYbT1z2f10eiF4T6QsqUVXEko4xQWsnqk9hDpIgREM/CAHRxgA98IQBqIH9sULQTddSBXNOj1IQcw5EwChpIQGiitZbE+t5kGMCjloHnBxevsCRgQHRRmRcOwVcwPjTVpeZ/w+WySKl4SL2HtLfrl7QkqIrBZJGhRkL5n3/hgFT5uRa/l7QDy96/NQsvpiSCFcTrOJTMYWIHtpShFs2oIuB6l40mqRkV+793zuNexhRO5AN/4WsLOndiItyEJy1oGoSQUjYyZiDQSbASA6AcNJowoTk6Acrlp4JhsFDYntILNdzA5eIgHMeJHrCBQEEPila5f1Y+4SATIQaHYBAe0qQIYswvNHp/OD/PI82101ge/SV5pIS2HL0zWR3w40wq94mwxUaIC2vagc+RysWjwNNq6r2sbYUGhCbZyVN6nYQCPJ6FD86jRc5iHbZE3wzAyGekwSE2w0tEfR92sN0RNMQf0s5yyDEMNTecMC6odcbR4GNjWT8nXM9nCGQbOk3lJIFwinHaqXZO2HLc/mRdg8eUwym4qisIsBQEGEGRkFgCDAFkFBFCACKsWQSIyRIKRRGCMF0TXUPhyTvw9x4acvgn6MwcUa7RPzfLPH0sIUVEoycRPesDORDghm7ogWNOMQvKywmYdeFq7T9B+DbffcVF/VS8WsM3v9aaVNi0VLz2uuEbJQnvKZ4oKEQQ2lUOUje3CNujePD2ziuWh/ytuCz+SN3llo4RunhFaT8trUF6T6gORVBRVARaoo2kWK0I1/gdvV1dXxEsBZTkpnKEgU0pAhEKIlhXDMGeMSU5NexlS4FK3ADSsWEzFLCBRBJcMOYw75eAOAYfDvox8YYpUx84/ptY9sun5HiAkYLIp9dPpnyU09Er7HjZHDD9IOO11XRIcdP+AVC4NPqS3qlRPsXtaKEfjZTVmt3wtcp3TmP4QIyLOpfAoLjlt6LKbpLbabG0axp795U1+hVloS1CbFrEqOZ39yGxWUT+ckGvsdm9/R5FsBDIiHWBDFIDnxGZlpFgwRQR0hsXWSBpgMEsbsUgJIJKwoxtikWCjiFNOM9BiAmfVJQ49VDDE0QmFSRlhjRHGNrgoFuqEpC7OxrdM8G93UI+IrbvaMuGlXkq00aQxBBgmaKqDbHSDfXA2aWlqHLChhItAyGknX1FaukhoqbOKp0ZrCxo8GFDATZOhEhkVoPsCKfURhH0I/aQQMAA3ajh3V6t9GHeWw6dqaNrvC0AzmpOIU7fxqF0XIIQhxlEgDJISEPANOvZ3BxU2GDkIZwWQQL0GCYNKBBylB35TDDHHqDIELIOGoEIUbiLKoHpPHVmUZGKJ0JRha9lKEPqdwnbThCw/E5u9TB4et5dlzaRDd4JM2pqOxsyed4upjiJFi4JjeIt4G/4Q6Gz1Y/Sig9sUF5EO7Sd52Ih65EQMXXIh6MNxgOZDqHCgsY9PKUINjVULamwKGAyHR3SrqzQOUso0TVC9tC5QqMsxbQ0CB7vvmG6K9iVlHo949foa3WclkhYIT28HuuDQ9lOvX8KFtCxDomosY/cNtstNdEX8cKfJDZbuQ+EGGglEikgjTe5OxVQ9LevxpzQ0hRrJ3eNmmrx+KmoiImhoVr/AmY/1uOVtatkqbiXLmV4xvzL/MdTk5ePDoA9LDkeM8AoPIIp7Al5hhgk+uzfA0k92rvHFHLYwVgjCWMhTBbgCNyhH1gB8gD+phO4/TkBoQ0RYaD3QCDBZCxFgdu6lcAYl6iAkgk8FCtgQfibXQBmjUB0wjEQ+UV+9hucA45OyTt6y1p1aA+RBfx7vAOJuPgZ+CKfxshxIes9DANApGRVkBGS5KIiupmFfW6RJUbhhAUqkKKQNAwFoQdWAIYKXY3gNRKgsgNQIUGWlAsBgQRhFIxKktBsFhIshIKLFAsAsZZRAsGAhQEYFKEFSAQghGIRad4c0gsFJAJEc5q9RT74jthEg2h5CgtaJi98hOgfBO/cyQg4HfvVWGDUwQylPnbyHPpuqwOzMyx9x3247MqPr+uL8z0cZByQ283ISKxSHLY34zuvLjwvnzMvKHRkGTjlnC0F6Vhr9W8QoJb8tsJZB9DNud8i9+1xXyTZXidvAo0zT/cbDA2/VX7rZn5HbIjZI34XfbkjFb/dKkYNYwRnRDYdktBPsY8vsnEHWDqDM2HgQ4lc4JUDrLTkm6jjNRCTFB6ylFDMjm/NRTDl2QEgtJvNMOm6RSZuEdruR1odo6dRMF/ss1PoqTW2Odo3LagpFczRZLlTn94+gd226Sx88aqKoWphvHMHkyI9p3qR04eOqKVc4tZU4NcTC8UHeH1veb0E7020u8OEt4Zt9B9MN8TdHdSZsdVM9ZbjPiruZZNCH42KjJpXEW9LT0jbU7hRbQnrKx7t5tZWQ5eQZDQ7f1PfMEu5wTYHGsFjriJho43mqD82ZWI4to1NG+LrIJitGuLA4nbYeuhQ0GndJCqNq+alZaTsoZj7+T9tuC1w7bWUaih3QDAPMZ8+01NyGgNULKjlboC4OTnAXkXArDMNDMR6uhQkVLA4IEZeSbFtkT7acSaUB5oWep5VUiXuGFcdRGD44PDoVOoUNS9JPh68Xuj1EhcJ0I4lFQxuP/StrmrNFpXbhUM47jfGbNGW3hWkqTJJOyQ+RVuWrTJCTaJuBE5EHVvtpB0KnUkXPu9aiaS5fcGocowhaZVM+0vzQMEDCGGl3Aa8uw+g+LCa8QzHNgxdFz8ZLikDvWtXRZVu+xhrjh6f0cDINXbmzPQwtPpj8sA0qENOiC5PDzGku3cgpiFORwKQySMhNMpkEbpv3popMhYeuGJSzdKIipEEGAuFoCm9sFhHQCIygbbFgKQVAr2RGndK4ESgKCgab92uDPenQwbSrfClUXUvLRbQ3MZVDv+J1ELlwmgxISCfxeVFztzMZUKLhZbYR+vVEdtSwdgFSosGIDNUihcKVrCo1C6MAKGI6+MdaTUuLs0XEqmqFbkIpCacFSa1mamhtllovXZBQym7t2VlMxBuvKq1RqurLaRwikeOqgXCNoPitYsSSO9OXSe+J6MOQ7d1Clu58WIwgc0DogaIdDSQWhbfZ9d61PbhzxcpjMbVZbLCe07nwFTUhuGIZGLkWViYCj8sE7GB4qqIrFWPpezchfht/pcU07nt32N3x4ER6jyIebIRgydN1YeAW3EjGBAsWA4AtojQ40h1Ga3ehm+/qVbix2WcYgP2m17UDzSEimMM2X8oIEIZA60Nqvoo2kdzCjn7IdZ9ExFC7IheUs6GeCeZrAJw2JI1UbM5Y208TXPe1i6SLpMY2MgoIixiDER4FY3EMh9AmTIUiJRKNZWLCAhIggIAshBHzoVWHrhAsmQMsCyX0XGUxPQNReLSiVkS0XeIjWx4wg2mnlUKN1REYZaloWEwtcYTEAqSXMGrIIVC0IJgxaHEim0pXMVKddizsjRFqXrsfZZ2ppHaT6OPecjzDWnlacg0LsjWaeiSLkOh0gqcowox1w3BRpNiXgsMdFIxJQK80R3mixRdUqJRVUbXWEW7dv+LUKJexIBWkWj0T3ATKF8SSZs7KXdQ4pCHPNFh/STfphjbTKTOSlVAolkCLmBn4m85BoHBjBNAX0Dtgd7NhNIMLBt4XwZhmVA9NoFsR6KaCA6kbWCPkvJDTCx89na3tNRC6LamVIxx0K9yqCmgiYfV4a3v5+PG1zesS6tXjbKpeM4EOZ9Vt4419exx9J9/UdWC6WQz+xYHOlW5p+oliUwzmoPVbyy1qm/MMIOWYZMCXm5Lzx5iMNNGg0wTiJP1U2WhJNNhIvN50aiEam8ly3MNORvZcjgOh6+9w52cWxZl2T9dOBLCOOQcmqPOWJgdBaA78crowpeH563oymMduIwmS6Cmlmdx3bvxxAqBxM0rYmOxBy+dwYNHXEw61EgeNNdJwlc3DOqO9qGIycwy5yoKcc1dtHCGPrhpoic6jxhQpNxjhskTpU8w7wgompJIRRpQTa+/XFJXNGt97wLGAxggNjERiXQaCKNsSNeDO4E4qT3KMBzyTgQDljEG5cmcUCbswYok5qPIwOTK8cUDaGqlE6OHGx1BLPGFFMgbaIE2lSYoquJyfbFet3GhD6mOZqaKhJPUjijbv3b3cEY25I7UFPQhZMMSZMtMadjrYOzNg9nv2caws54cs52skZdVokt0dP4ltvFSOwyL7TkzHLHuhoaGJprZINNHBhuCMwUoTTBRiE0kZShnqDWhE6pTYwagbHUWjZ1EwDlMVVG5NEfDplkWa0nThVTO5eKI48pit8Q+VrKjHHvhhguGEhmxkdGggaELAqjY0JpXQVQaIgOy43XRKlkoe8k0hKPJyUbQ6zBwlKBzUuRNVlmxvSsunq10aw0SYLQIjTMIhMcTTGwZzM74xiTQdeia3BUYU3mUlDYihisUKyKIWFNgQJ4eHqbZwEYWNbA4oEcOJ0ad3ptouSYZYZ0DVx2fnqOJizSRs5pCIhY3gGIB+hI7o6emgvIQFkVcgzliw5tMxMuWRinSnJ3g3NAYIoQio2MNdKJfWUxMo5TDN3TA0IhplGMEiJYjhYUshYsFkVqZcRQTQMICxhJIDGkliKQFCiqVKHYOpod87uDlwSrNVbSoq0zIYJqU0ihjdVJQEFDoNDDEgpo8w6y62NfgrLDJEDmQDkUgc8pFwOxvweEex0yrR1gco0dBJ/GKPTg/f6U6KsA8wYxEShFpT4Pq2s1sXJaMYZ7LRDQA6iexUmEhXMo6EiEHYBg6OmhzwSsCt6ZOEkOW7eYHQg6Rgv0RQKVKgK0Q7YpSGZUCN1liRRHJ+/DBXX1DEIN1IFRJHh0IoM5Aw9s34ooPkoQDHlPwcE3VYxyoLkHvPV3BVgKhUOPK7NAhATuZyiCovUYbdZ2+7vpmksHaeOQnBBvBgXALUa422FTJAoVFGadOEpJo3wKIM0FhZujTJcjemkDsNJp0+Q4fm3dsMZDF6LEjRsaylFHPtfJNh013rF3wyQnbIdwMhuRU6wgHTept8BMDMmKQOya4CHGIOkhzKetVVXyEO0UdpOPmmLURhXZ/AdW2Gw8jzstsDbKqirDkQpRGHgJQGwQRH0pQIe83E7Az11rb6ZtTbs6Zj3m1RzhALKmmiwHZkZ4ZuJ2iZlDquWNBGCggIAyT4n5Dpt5KsGanG8h+gQ5JBNqEETgEAzC5g5bzSHyffv8Xko02YURoYyUzXYPE8fQwrXBlyHOVayINtGYSNc0I2wcacy7qqRWTSpIGqBWhwogl7oQu4SjCQqVelOqatvXmzL6DS87hEkGgWgomwShA9QbF1gcgxFvpm4VsohaAEgZAQCg2gb9iWNWU2RSHDsJCEMaMe2rksePpEOae/zqlB0IcqLL1onA5RzE7YrmhhW4KVMwwz2aIQuFC0gyLBUYxEYTaNQNNtqM5kRv/s2IREkBG/mzEQ5EAoJdDED1D7bgaxdQrnJyDIXnFSBJB95AWEHbCfz8dIQ94Gic4CE/4Gx2WiKhERpSMUJEDJBdURtTE0CwUC8g9WHvlC8PGJo9jiGxfTifD20UH2wuNVmNuQlqn/3o3ogtpfaWXxMn5kglrGiAYEc85odauTYfBYWHMK9cFXALJjXkYBIWSD2FWJEOQXQjw6urTbUVsKeFBVFiyR0mdVT80JQbEb86wJGEeZmO5MSJDXqrqgaggO4jD5QvlPd+R97fm75ANkYL/ytVmWzLJRYCgsRIEUiQ2oRmkDPGWSgyxEoBuy0gzyag1dXR9ZBCEzKFyfk7LJ5uNTBrmcaPqnxDAfSL9oRSRxOw7s6RmZeiRWJGffhzJ322344UYswBYMoWQsHz1lgAx4Nfo4kKZqfFMy8rBSF1tkibW5C/IvpgJMQVSMc4gM4YHJDhpPDMeV5lULkwgSLM30BYMs2NH5C6YsMhpMhsKNDqPiDkO6Ic54GYzB3mYwF5BH1mEIQoiKIiQsMsMApPrWkQfQ/1+M/Dz8XizH3fyYxi+kcpsR7EokXiOwA2lAZXwyocOguUtrMYh+whe1BE8nzU5TkcDxhqOPPvS1AR7pCX5SmoH0USKhBkgrIoFEIVCSid6AxRgoQFCYyHxigkNz3Hv1GWQgl2M/E40H6Pw7CZEbMt/ijZvKx0fYmKR+UWtwWjPuHbDdBijh9n2i8Ijt22OcjQy08xs0k3vb0osMEqDqmwaseASwZUQ9FObj9NzeEOiEyR4Q8lshUDiHMprN5ANLtGwaEi9BA8tkTxhcRN2lEywcoZ0MlFzmR0aclX3wXAPuOQGcgJtNnm9NHdzPbZ7oScR1Fy076WEP84fLAyiTzn0BYwffPwHhmbSu/gGoCCC1jyGcvefFojP9/AlR8xU+07eLXlS89Xn/cUqzh/YVJfKXGDGThmNRMv/1D/p7PT/vP/4u5IpwoSF/BRlGA')))
\ No newline at end of file
diff --git a/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/problems.py b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..60c2a01aa4cdb29a69db4d11a1ee68df1b7e3dc2
--- /dev/null
+++ b/examples/presentation/student_handins/unitgrade-docker/tmp/intro_python/problems.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    assert(False)
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/examples/presentation/students/cpp_course/fractions.py b/examples/presentation/students/cpp_course/fractions.py
index 6c3082856804b6a7cfc394a3acb8b68ca4331e09..b13ef5894ff3236c5a3aa0775d1c7e46d93533b5 100644
--- a/examples/presentation/students/cpp_course/fractions.py
+++ b/examples/presentation/students/cpp_course/fractions.py
@@ -25,7 +25,7 @@ class Fraction:
 
 def from_string(s):
     """ Convert the string s to a Fraction(a, b) object. """
-    # TODO: 10 lines missing.
+    # TODO: 11 lines missing.
     raise NotImplementedError("Compute and return a Fraction(a,b) object corresponding to s.")
 
 
diff --git a/examples/presentation/students/cpp_course/tests_ex6_grade.py b/examples/presentation/students/cpp_course/tests_ex6_grade.py
index 7c7e2a4862eeeb9ff895be658668664128d8b954..fd11dbb77c1ef8459f783a76addc835596e4ecd1 100644
--- a/examples/presentation/students/cpp_course/tests_ex6_grade.py
+++ b/examples/presentation/students/cpp_course/tests_ex6_grade.py
@@ -1,4 +1,4 @@
 # cpp_course/tests_ex6.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/students/cpp_course/unitgrade_data/Fractions_Basics.pkl b/examples/presentation/students/cpp_course/unitgrade_data/Fractions_Basics.pkl
index 399069ef78aabbd5c563ea3c29de826589501450..2d31d464ed020fd09e00880bef601fc4392ecb9e 100644
Binary files a/examples/presentation/students/cpp_course/unitgrade_data/Fractions_Basics.pkl and b/examples/presentation/students/cpp_course/unitgrade_data/Fractions_Basics.pkl differ
diff --git a/examples/presentation/students/cpp_course/unitgrade_data/Fractions_from_string.pkl b/examples/presentation/students/cpp_course/unitgrade_data/Fractions_from_string.pkl
index b01491f2121c122ac1e340a69286cd1a20b39b59..a0e39d1e2fab811d26708601122b4ec6c5dd3ec2 100644
Binary files a/examples/presentation/students/cpp_course/unitgrade_data/Fractions_from_string.pkl and b/examples/presentation/students/cpp_course/unitgrade_data/Fractions_from_string.pkl differ
diff --git a/examples/presentation/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl b/examples/presentation/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl
index d9f67346343978b015fde99b56b6ab5c85017ca5..77cbbfcab4b630fddc890be3158e1cf5b05050b8 100644
Binary files a/examples/presentation/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl and b/examples/presentation/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl differ
diff --git a/examples/presentation/students/cpp_exam/tests_exam_grade.py b/examples/presentation/students/cpp_exam/tests_exam_grade.py
index 6aa0d1911e814bbe3106605c7a6a01d9c5b5a669..d09bb1329d42fc29cd42b16524e54449d5669ae7 100644
--- a/examples/presentation/students/cpp_exam/tests_exam_grade.py
+++ b/examples/presentation/students/cpp_exam/tests_exam_grade.py
@@ -1,4 +1,4 @@
 # cpp_exam/tests_exam.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/students/cpp_exam/unitgrade_data/Q1Vectors.pkl b/examples/presentation/students/cpp_exam/unitgrade_data/Q1Vectors.pkl
index 1bb470ade1775939b45fbd6f3102d136b7f49eea..e5ab8f30ab6b477c352d83c3a1ea42450d158c0b 100644
Binary files a/examples/presentation/students/cpp_exam/unitgrade_data/Q1Vectors.pkl and b/examples/presentation/students/cpp_exam/unitgrade_data/Q1Vectors.pkl differ
diff --git a/examples/presentation/students/cpp_exam/unitgrade_data/Q2RLE.pkl b/examples/presentation/students/cpp_exam/unitgrade_data/Q2RLE.pkl
index 20cb684a1956677afbd6991a2e1805751ba9b13f..8bd8f93e1dcf62d022df12f97ee69a13daa3bd84 100644
Binary files a/examples/presentation/students/cpp_exam/unitgrade_data/Q2RLE.pkl and b/examples/presentation/students/cpp_exam/unitgrade_data/Q2RLE.pkl differ
diff --git a/examples/presentation/students/cpp_exam/unitgrade_data/Q3Groceries.pkl b/examples/presentation/students/cpp_exam/unitgrade_data/Q3Groceries.pkl
index 7bc62d9208bce50dc506d849658a180725a812db..e894bd9fcebfad69241890258bd985c0f234a37b 100644
Binary files a/examples/presentation/students/cpp_exam/unitgrade_data/Q3Groceries.pkl and b/examples/presentation/students/cpp_exam/unitgrade_data/Q3Groceries.pkl differ
diff --git a/examples/presentation/students/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl b/examples/presentation/students/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl
index 46b03afee78f6a519772733cb725d16b7c852dc8..8544aeb6c5ac91153ee70063ef229218461e7344 100644
Binary files a/examples/presentation/students/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl and b/examples/presentation/students/cpp_exam/unitgrade_data/Q4FilterBuffer.pkl differ
diff --git a/examples/presentation/students/intro_python/exam.py b/examples/presentation/students/intro_python/exam.py
index 02b54dc010ec0b19a666967b5b4adf2978a5cbb6..d0eaba4d28d7dd2792bed596c0ad62a8e20cd80a 100644
--- a/examples/presentation/students/intro_python/exam.py
+++ b/examples/presentation/students/intro_python/exam.py
@@ -11,15 +11,6 @@ class Q1_WaterHeight(UTestCase):
         print("Water height computed to be", h, "should be", self.get_expected_test_value())
         self.assertEqual(h, 3.0) # Check the height is 3.0
 
-    @hide
-    def test_water_height_hidden(self):
-        checks = [(120, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
-                  (12, []), (14.2, [8.8]), (0, [0.8]),
-                  (3, [0, 1, 2, 0, 0, 5.2, 6.5, 7.1, 0, 0, 0.1, 1, 2.9, 0.13, 0, 2, 0, 0]),
-                  (0, [0, 5, 2, 0, 0, 5.2, 6.5, 7.1, 0, 0, 0.1, 1, 2.9, 0.13, 0, 1.2, 0, 1.2, 7.5, 0]),
-                  (0, [0, 0, 2, 2.1, 2.4, 2.2, 2.5]), (18, [30, 1, 28.8]), (1, [0.5]), (2, [])]
-        for h0, r in checks:
-            self.assertEqualC(water_height(h0, r))
 
 class Q2_AstronomicalSeason(UTestCase):
     def test_seasons(self):
@@ -27,10 +18,6 @@ class Q2_AstronomicalSeason(UTestCase):
         print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
         self.assertEqualC(season)
 
-    @hide
-    def test_seasons_hidden(self):
-        for d in ['27/12-1998', '21/06-2108', '08/05-1998', '07/08-1945', '22/12-1208', '19/03-2001', '23/09-2018', '21/06-2008','12/04-1964', '13/01-1900']:
-            self.assertEqualC(astronomical_season(d))
 
 
 class Q3_TimeAngle(UTestCase):
@@ -39,11 +26,6 @@ class Q3_TimeAngle(UTestCase):
         print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
         self.assertEqualC(a)
 
-    @hide
-    def test_angle_extended(self):
-        for minute in [0, 15, 18, 20, 34, 50, 59]:
-            for hour in [0, 1, 5, 6, 10, 12]:
-                self.assertEqualC(time_angle(hour, minute))
 
 class Q4_TicTacToe(UTestCase):
     def test_tic_tac(self):
@@ -54,21 +36,6 @@ class Q4_TicTacToe(UTestCase):
         print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
         self.assertEqualC(score)
 
-    @hide
-    def test_tic_tac_hidden(self):
-        boards = [[[1, 2, 0], [1, 2, 0], [1, 2, 0]],
-                  [[1, 1, 1], [2, 1, 2], [2, 2, 1]],
-                  [[2, 0, 1], [2, 1, 0], [0, 0, 2]],
-                  [[1, 0, 2], [0, 1, 0], [2, 0, 1]],
-                  [[2, 0, 1], [0, 2, 1], [0, 0, 1]],
-                  [[0, 1, 0], [0, 1, 1], [2, 2, 2]],
-                  [[1, 1, 2], [0, 2, 0], [2, 1, 0]],
-                  [[1, 1, 1], [0, 2, 0], [0, 0, 0]],
-                  [[1, 2, 1], [2, 1, 0], [2, 0, 1]],
-                  [[0, 0, 0], [0, 1, 0], [0, 0, 0]],
-                  [[2, 1, 1], [1, 1, 2], [2, 0, 0]]]
-        for board in boards:
-            self.assertEqualC(tictactoe(np.asarray(board)))
 
 
 class Q5_StandardizeAddress(UTestCase):
@@ -77,11 +44,6 @@ class Q5_StandardizeAddress(UTestCase):
         print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
         self.assertEqualC(s)
 
-    @hide
-    def test_standardize_address_hidden(self):
-        for address in ['Kongens_Lyngby_2800', '10000_Zagreb','43500 Daruvar','Egtved_6040','Vejle 7200', '02108_Boston',
-                  'Pasadena_91001', '90001_Los_Angeles', 'San_Francisco_94016', 'Rio_de_Jainero_22775']:
-            self.assertEqualC(standardize_address(address))
 
 
 
diff --git a/examples/presentation/students/intro_python/exam_grade.py b/examples/presentation/students/intro_python/exam_grade.py
index b5478483bcc6f0eb8a174cd68889350a6681af80..67b272c3fa4478273b3f9ea2a69d03e824a5ae17 100644
--- a/examples/presentation/students/intro_python/exam_grade.py
+++ b/examples/presentation/students/intro_python/exam_grade.py
@@ -1,4 +1,4 @@
 # intro_python/exam.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/presentation/students/intro_python/unitgrade_data/Q1_WaterHeight.pkl b/examples/presentation/students/intro_python/unitgrade_data/Q1_WaterHeight.pkl
index 959b3da92d0109166386114384e2283c87bb0c86..834d08b7bbdcf8ebf2e06cfe57953e687871396a 100644
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diff --git a/examples/presentation/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl b/examples/presentation/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl
index ded5dd0d2b52198997ef58794fc3ffaec0fe37b7..2d677c3561d435b085828c376c30118f918b7c5d 100644
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diff --git a/examples/presentation/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl b/examples/presentation/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl
index 50090e6cc4db269029576997d4da40c4176dd3c2..8528aeef3c8c029a9a8af2b1bc198d19a0463555 100644
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diff --git a/examples/presentation/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl b/examples/presentation/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl
index 8c6eb03977b19b14b4341b68c8a96b0b1baa5808..163e3b6bbf380c2ec67ca64c32d89bee735a97be 100644
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diff --git a/examples/presentation/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl b/examples/presentation/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl
index e013c45a0f8b0e56fbf974cd7ff8bba3583dfe5d..a55324933d96a979dce39176c27427e4ef8b5ea2 100644
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diff --git a/examples/presentation/students/intro_python/unitgrade_data/cache.db b/examples/presentation/students/intro_python/unitgrade_data/cache.db
new file mode 100644
index 0000000000000000000000000000000000000000..8dd368fef0b15c12edfb4affc508d49a8b00b794
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diff --git a/examples/presentation/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl b/examples/presentation/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl
index e45319db9112fdae5426c258ccf3b595dcb2df0f..d323d4cf41dd5b63d5f45f495924e2f57c13bc78 100644
Binary files a/examples/presentation/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl and b/examples/presentation/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl differ
diff --git a/src/unitgrade_private/__init__.py b/src/unitgrade_private/__init__.py
index cd56efa2d9dc110ce754ae04fbbd9ff4f5b2ae1a..ac3b0a0204898ff77859ca6214502ad225b530fc 100644
--- a/src/unitgrade_private/__init__.py
+++ b/src/unitgrade_private/__init__.py
@@ -1,6 +1,7 @@
 import os
 from unitgrade_private.version import __version__
 from unitgrade_private.hidden_gather_upload import load_token, save_token
+# from unitgrade_private.deployment import remove_hidden_methods
 from unitgrade_private.plagiarism.mossit import unpack_sources_from_token
 from unitgrade_private.hidden_create_files import setup_grade_file_report
 
diff --git a/src/unitgrade_private/deployment.py b/src/unitgrade_private/deployment.py
index 65a618a9bb0871adc83a65556ca270fae98462bc..57d1e263e1d49e7368d488dbf7013d59912c1bfd 100644
--- a/src/unitgrade_private/deployment.py
+++ b/src/unitgrade_private/deployment.py
@@ -28,7 +28,7 @@ def remove_hidden_methods(ReportClass, outfile=None):
 
     if os.path.exists(outfile) and os.path.samefile(file, outfile):
         raise Exception("Similar file paths identified!")
-    with open(outfile, 'w') as f:
+    with open(os.path.dirname(file) + "/" + outfile, 'w') as f:
         f.write(source)
 
     module_name = ReportClass.__module__
diff --git a/src/unitgrade_private/docker_helpers.py b/src/unitgrade_private/docker_helpers.py
index 0b82a931c0268a356ee54f777fbc7ed0ef095e3f..75ce1a589c9cc706957d742a9c9462b9f2339428 100644
--- a/src/unitgrade_private/docker_helpers.py
+++ b/src/unitgrade_private/docker_helpers.py
@@ -21,6 +21,7 @@ def download_docker_images(destination=None):
     zf = zipfile.ZipFile(result)
     zf.extractall(path=ex)
     dockers = [f for f in zf.namelist() if f[-1] == "/" and len( [s for s in f if s == "/"] ) == 3]
+    images = {}
     for f in dockers: # zf.namelist():
         tmp_dir = ex + "/" + f
         if os.path.isdir(tmp_dir):
@@ -31,6 +32,9 @@ def download_docker_images(destination=None):
             else:
                 print("> Extracting docker image", os.path.basename(f[:-1]), "to", dest)
                 shutil.copytree(tmp_dir, dest)
+        images[os.path.basename(os.path.normpath(f))] = dest +"/Dockerfile"
+    return images
+
 
 
 def compile_docker_image(Dockerfile, tag=None, no_cache=False):
@@ -195,4 +199,5 @@ def docker_run_token_file(Dockerfile_location, host_tmp_dir, student_token_file,
     if len(tokens) != 1:
         print("Wrong number of tokens produced:", len(tokens))
         print(out)
-    return tokens[0]
+        # assert False
+    return tokens[0], out
diff --git a/src/unitgrade_private/hidden_create_files.py b/src/unitgrade_private/hidden_create_files.py
index b0d0affb804a5b5531a0d083078aed85b5ab41dd..c9091383f9170cfe0511a73f83f48b4924690ade 100644
--- a/src/unitgrade_private/hidden_create_files.py
+++ b/src/unitgrade_private/hidden_create_files.py
@@ -5,10 +5,9 @@ import inspect
 import time
 import os
 from unitgrade_private import hidden_gather_upload
-# import sys
+from unitgrade_private.deployment import remove_hidden_methods
 import os
 import glob
-# from pupdb.core import PupDB
 
 data = """
 {{head}}
@@ -42,7 +41,19 @@ def lload(flist, excl):
     s = rmimports(s, excl)  # remove import statements from helper class.
     return s
 
-def setup_grade_file_report(ReportClass, execute=False, obfuscate=False, minify=False, bzip=True, nonlatin=False, source_process_fun=None, with_coverage=True, verbose=True):
+def setup_grade_file_report(ReportClass, execute=False, obfuscate=False, minify=False, bzip=True, nonlatin=False, source_process_fun=None, with_coverage=True, verbose=True,
+                            remove_hidden=False, name_without_hidden=None):
+    if remove_hidden:
+        name = os.path.basename(ReportClass.mfile())
+        if name_without_hidden is None:
+            if name.endswith("_complete.py"):
+                name_without_hidden = name[:-12] + ".py"
+            else:
+                raise Exception("Must spacify name to give new report file")
+
+        fout, Report = remove_hidden_methods(ReportClass, outfile=name_without_hidden)  # Create report3.py without @hide-methods
+        return setup_grade_file_report(Report, remove_hidden=False) # Create report3_grade.py for the students
+
     print("Setting up answers...")
     url = ReportClass.url
     ReportClass.url = None
diff --git a/src/unitgrade_private/pipelines/dtulearn.py b/src/unitgrade_private/pipelines/dtulearn.py
new file mode 100644
index 0000000000000000000000000000000000000000..77d5ac41034613256ce0ad70824b1ddae90bac3e
--- /dev/null
+++ b/src/unitgrade_private/pipelines/dtulearn.py
@@ -0,0 +1,425 @@
+import shutil
+import os
+import glob
+import time
+import openpyxl
+import setuptools.command.install
+import tabulate
+from unitgrade_private.plagiarism.mossit import moss_it, get_id
+from unitgrade_private import load_token
+from collections import defaultdict
+from coursebox.core.info_paths import get_paths
+from unitgrade_private.docker_helpers import compile_docker_image, download_docker_images
+from unitgrade_private.docker_helpers import docker_run_token_file
+from coursebox.core.info import class_information
+from coursebox.core.projects import gather_instructor_sheets
+import openpyxl
+import numpy as np
+import pandas as pd
+
+
+def write_summary_report_xlsx_file(write_html=True, open_browser=True):
+    """ This function write a summary of the students' performance in the three reports to a XLSX-sheet. """
+    projects = [1, 2, 3, 4]
+    code_part_weight = {1: 0.5, 2: 0.4, 3: 0.7, 4: 1}
+    report_weights = {1: 1, 2:1, 3:.5, 4:.5}
+
+    paths = get_paths()
+    info = class_information()
+    gather_instructor_sheets(info)
+    # Now sheets are saved to main xlsx-file. This file reflects all handins.
+    info = class_information() # Reload information to make sure the report field is set. This gives all available reports.
+    tres = {}
+    # p = 4
+    # handle_project_handins(project_id=p, moss=False, docker_verify=False, verbose=False)
+
+    for p in projects:
+        try:
+            tres[p] = handle_project_handins(project_id=p, moss=False, docker_verify=False,verbose=False)
+        except Exception as e:
+            tres[p] = None
+    dd = defaultdict(dict)
+    for j, (id, v) in enumerate(info['students'].items()):
+        def add(name, value):
+            if isinstance(value, float):
+                value = np.round(value, decimals=3)
+            dd[id][name] = value # .append(value)
+        add('id', id)
+        score = 0 # Compute the total score.
+
+        for p in projects:
+            if p not in v['reports'] or v['reports'][p] is None: # This is because report 4 has no handin evaluated by TAs.
+                group_id = -1
+            else:
+                group_id = v['reports'][p]['group_id']
+            add(f'{p}: group-id', group_id)
+            ta_score = v['reports'][p]['pct'] if group_id > -1 else 0
+            # code_score = tres[p][group_id]['student-token-total']  if group_id > -1 and group_id in tres[p] else (0,1)
+            if tres[p] is None:
+                continue
+            if p == 4:
+                code_score = tres[p][id]['student-token-total'] if id in tres[p] else (0,1)
+            else:
+                code_score = tres[p][group_id]['student-token-total'] if group_id > -1 and group_id in tres[p] else (0, 1)
+            add(f'{p}: did_handin_code', code_score != (0,1))
+            add(f'{p}: did_handin_pdf', group_id != -1)
+
+            code_score = code_score[0] / code_score[1]
+            # print("RPadfsf")
+
+            add(f'{p}: ta-score', ta_score)
+            add(f'{p}: verified-token-score', tres[p][group_id]['verified-token-total'] if group_id > -1 and group_id in tres[p] else 0)
+            add(f'{p}: student-token-score', code_score)
+            add(f'{p}: student-token-location', tres[p][group_id]['token_location'] if group_id > -1 else None)
+
+            score += report_weights[p] * (1-code_part_weight[p] ) * ta_score / sum(report_weights.values())
+            score += report_weights[p] * ( code_part_weight[p]) * code_score / sum(report_weights.values())
+        add(f'TOTAL SCORE', score)
+
+    d2 = {}
+    d2['key'] = list(dd[list(dd.keys())[-1]].keys())
+    for k, v in dd.items():
+        d2[k] = list(v.values())
+    print(tabulate.tabulate(d2, headers='keys'))
+    try:
+        for p in projects:
+            print(p, len( [k for k, v in dd.items() if isinstance(v[f'{p}: student-token-score'], tuple)] ))
+    except Exception as e:
+        pass
+
+    print(tabulate.tabulate(d2, headers='keys', tablefmt='html'))
+    fout = paths['semester'] +"/report_summary.html"
+    if write_html:
+        import pandas as pd
+        df = pd.DataFrame.from_dict(d2)
+        from pretty_html_table import build_table
+        html_table_blue_light = build_table(df, 'blue_light')
+        # Save to html file
+        with open(fout, 'w') as f:
+            f.write(html_table_blue_light)
+
+        df.to_excel(paths['semester'] + "/legacy_evaluations_" + info['semester_id'] + ".xlsx")
+        print("Saved output to", fout)
+    import webbrowser
+    if open_browser:
+        webbrowser.open_new_tab(fout)
+    import pickle
+    with open(paths['semester'] + "/legacy_evaluations_" + info['semester_id'] + ".pkl", 'wb') as f:
+        pickle.dump(dd, f)
+
+    return dd
+
+
+# def handle_project_handins(project_id=0, moss=True, docker_verify=True,verbose=True):
+#
+#
+#     return res
+
+
+def docker_verify_projects(learn_zip_file_path, Dockerfile=None, instructor_grade_script=None):
+    dzip, tokens = unzip(learn_zip_file_path)
+    # paths = get_paths()
+
+    if Dockerfile is None:
+        images = download_docker_images()
+        # os.path.isfile(images['unitgrade-docker'])
+        Dockerfile = images['unitgrade-docker']
+
+    # info = class_information()
+    # Dockerfile = paths['02450instructors'] + "/docker/Dockerfile"
+    tag = compile_docker_image(Dockerfile)
+    print("Docker verify project image tag:", tag)
+
+    if not os.path.isdir(dzip + "/verified_tokens"):
+        os.mkdir(dzip + "/verified_tokens")
+
+    res = {}
+    # Create a logdir.
+    if not os.path.isdir(dzip + "/verified_tokens/logs/"):
+        os.mkdir(dzip + "/verified_tokens/logs/")
+    if os.path.isdir(dzip + "/verified_tokens/problematic_logs/"):
+        shutil.rmtree(dzip + "/verified_tokens/problematic_logs/")
+    if not os.path.isdir(dzip + "/verified_tokens/problematic_logs/"):
+        os.mkdir(dzip + "/verified_tokens/problematic_logs/")
+
+    def points_from_name(token_name):
+        return token_name.split("_")[-3]
+
+    for id in tokens:
+        stoken = tokens[id]['token']
+        if 'verified_token' in tokens[id]:
+            if tokens[id]['verified_token'].split("_")[-3] == tokens[id]['token'].split("_")[-3]:
+                print("skipping", id)
+                continue
+            else:
+                print("Token points obtained did not agree, re-running:")
+                print("> token   ", tokens[id]['token'])
+                print("> verified", tokens[id]['verified_token'])
+                for t in glob.glob(os.path.dirname(tokens[id]['verified_token']) + "/*.token"):
+                    print(t)
+                    os.remove(tokens[id]['verified_token'])
+
+        t0 = time.time()
+        # ig = paths['02450students'] + grade_file_paths[project_id] #"/irlc/project0/fruit_project_grade.py"
+        instructor_token, out = docker_run_token_file(Dockerfile_location=Dockerfile,
+                                                      host_tmp_dir=os.path.dirname(Dockerfile) + "/tmp",
+                                                      student_token_file=stoken,
+                                                      instructor_grade_script=instructor_grade_script,
+                                                      xvfb=True  # Run X11-display (requires that docker is run on Linux).
+                                                      )
+
+        if points_from_name(instructor_token) != points_from_name(tokens[id]['token']):
+            with open(dzip + "/verified_tokens/problematic_logs/"+id+".txt", 'w') as f:
+                f.write(out)
+
+        # Just write the ordinary log file.
+        with open(dzip + "/verified_tokens/logs/" + id + ".txt", 'w') as f:
+            f.write(out)
+
+        if not os.path.isdir(dzip + "/verified_tokens/" + id):
+            os.mkdir(dzip + "/verified_tokens/" + id)
+
+        shutil.copy(instructor_token, dzip + "/verified_tokens/" + id +"/" + os.path.basename(instructor_token))
+        print(f"> Verified {stoken} after", time.time()-t0, "instructor token", instructor_token)
+        res[id] = {'stoken': stoken, 'time': time.time()-t0, 'instructor_token': instructor_token}
+
+    print("--------docker_verify_projects completed. Summary:------------")
+    for id, rs in res.items():
+        print(f"> Verified {rs['stoken']} after {rs['time']}, instructor token {rs['instructor_token']}")
+    print("---------done--------------")
+
+
+def token2rs(token):
+    r = {'questions': {}}
+    for k in token['details']:
+        name = token['details'][k]['name']
+        r['questions'][name] = {'items': {}}
+        for key, item in token['details'][k]['items'].items():
+            assert key[0] == name
+            r['questions'][name]['items'][key[1]] = item['status']
+    return r
+
+def project_print_results(learn_zip_file,verbose=True):
+    dzip, tokens = unzip(learn_zip_file)
+    dd = defaultdict(list)
+    dd['question'] = []
+    rs = {}
+
+    for i, id in enumerate(tokens):
+        # if os.path.isfile(tokens[id]['token']):
+        r = {}
+        stoken, _ = load_token(tokens[id]['token'])
+        if 'verified_token' in tokens[id]:
+            vtoken, _ = load_token(tokens[id]['verified_token'])
+        else:
+            vtoken = None
+
+        r['stoken'] = token2rs(stoken)
+        r['vtoken'] = token2rs(vtoken) if vtoken is not None else {}
+
+
+        if vtoken is not None:
+            for j, k in enumerate(vtoken['details']):
+                vob = vtoken['details'][k]['obtained'] if vtoken is not None else -1
+                sob = stoken['details'][k]['obtained'] if stoken is not None else -1
+
+
+
+                if vob != sob:
+                    print(vob, sob)
+                    print(k)
+                    name = stoken['details'][k]['name']
+                    print(name)
+
+
+                    d = defaultdict(list)
+                    for kk, val in vtoken['details'][k]['items'].items():
+                        d['verified'].append(kk)
+                        d['verified-result'].append(val['status'])
+                        d['student-result'].append(stoken['details'][k]['items'][kk]['status'])
+
+                    print(tabulate.tabulate(d, headers='keys'))
+                    if name != 'Kiosk3':
+                        print(id, "A score could not be verified in the test", name)
+                    print("Bad")
+                if i == 0:
+                    dd['question'].append(stoken['details'][k]['name'])
+
+                dd[id].append(sob) # We go by the students score. This is in case of rounding issues etc.
+
+        dd[id].append(vtoken['total'] if vtoken is not None else -1)
+        r['verified-token-total'] = vtoken['total'] if vtoken is not None else -1
+        r['student-token-total'] = stoken['total'] if stoken is not None else -1
+        r['token_location'] = tokens[id]['token']
+        r['id'] = id
+        if id.startswith("Group"):
+            id = int(id[5:])
+        else:
+            id = id.lower()
+            if len(id) != 7 or id[0] != 's':
+                print("Very bad id", id)
+                # raise Exception()
+        rs[id] = r
+
+    dd['question'].append('Total')
+    if verbose:
+        print(tabulate.tabulate(dd, headers='keys'))
+    # Now sanitize the result. Replace internal learn-ids with 6-digit ids.
+    # info = class_information()
+
+    # Turn all of this into a big xlsx file.
+    make_xlsx_file(rs)
+    return rs
+    #
+    # res2 = {}
+    # for k, v in rs.items():
+    #     id = k
+    #     if isinstance(k, str) and len(k) != 7 and k[0] != 's':
+    #         # Look up correct student id from list.
+    #         for id, student in info['students'].items():
+    #             if student['initials'] == k:
+    #                 id = id
+    #                 break
+    #     res2[id] = v
+    # return res2
+
+def make_xlsx_file(rs):
+
+    Q = {}
+
+    for id, v in rs.items():
+        for token in ['stoken', 'vtoken']:
+            for q in v[token]['questions']:
+                if q not in Q:
+                    Q[q] = {}
+                for i in v[token]['questions'][q]['items']:
+                    Q[q][i] = True
+    # Now we got all of the questions.
+
+    # Dictionary to write to xlsx file.
+    d = {}
+    for id in rs:
+        d['Question'] = []
+        d[id] = []
+        for q in Q:
+            d['Question'].append(q)
+            d[id].append(" ")
+
+            for i in Q[q]:
+                d['Question'].append(i)
+
+                def gbk(token, q, i):
+                    return rs[id][token]['questions'][q]['items'][i] if token in rs[id] and q in rs[id][token]['questions'] and i in rs[id][token]['questions'][q]['items'] else ' - '
+
+                d[id].append(f"{gbk('stoken', q, i)}/{gbk('vtoken', q, i)}" )
+            d['Question'].append(" ")
+            d[id].append(" ")
+
+    import pandas as pd
+    df = pd.DataFrame.from_dict(d)
+    df.to_excel("token_evaluations.xlsx")
+    print(tabulate.tabulate(d, headers='keys'))
+    import pickle
+    with open("token_evaluations.pkl", 'wb') as f:
+        pickle.dump(rs, f)
+
+
+def moss_check(dzip, out, moss_id=None):
+    # Moss stuff.
+    # paths = get_paths()
+    # dzip, out = unzip(0)
+    if not os.path.isdir(dzip + "/submissions"):
+        os.mkdir(dzip + "/submissions")
+    if not os.path.isdir(dzip + "/whitelist"):
+        os.mkdir(dzip + "/whitelist")
+
+    for student_id in out:
+        token = out[student_id]['token']
+        if not os.path.isdir(dzip + "/submissions/" + student_id):
+            os.mkdir(dzip + "/submissions/" + student_id)
+
+        shutil.copy(token, dzip + "/submissions/" + student_id+"/"+os.path.basename(token))
+
+    # if not os.path.isdir(dzip  + "/whitelist/irlc"):
+    #     shutil.copytree(paths['02450students'] + "/irlc", dzip  + "/whitelist/irlc")
+    if moss_id is None:
+        # id = get_id()
+        from pathlib import Path
+        id = get_id( str(Path.home())+"/moss.pl")
+    print("Calling moss using moss-id", id)
+    blacklist = ['*_grade.py', '*project1.py', '*kiosk.py', '*pacman_demo.py', '*pacman.py']
+    moss_it(whitelist_dir=dzip + "/whitelist", submissions_dir=dzip +"/submissions", moss_id=id, blacklist=blacklist)
+
+
+def unzip(zipfile):
+    # paths = get_paths()
+    # info = class_information()
+    # d = paths['02450instructors'] + "/project_evaluations_" + info['semester_id']
+    # dzip = f"{d}/zip{project_id}"
+    dzip = zipfile[:-4]
+
+    from coursebox.core.projects import unpack_zip_file_recursively
+    unpack_zip_file_recursively(dzip + ".zip", dzip+"/raw" )
+
+    # Now copy to a more sensible format
+    out = {}
+    if not os.path.isdir(dzip + "/tokens"):
+        os.mkdir(dzip + "/tokens")
+    for f in glob.glob(dzip +"/raw/*-*"):
+        token = glob.glob(f +"/*.token")
+        if len(token) != 1:
+            print("Problems ")
+        assert len(token) == 1
+        token = token[0]
+        fname = os.path.basename(f)
+        id_cand = fname.split("-")[2].strip()
+        # print(id_cand, token)
+        if id_cand.startswith("Group"):
+            id = id_cand.replace(" ", "")
+        else:
+            id = id_cand
+
+        if not os.path.isdir(dzip +"/tokens/"+id):
+            os.mkdir(dzip +"/tokens/"+id)
+
+        dtoken = dzip +"/tokens/"+id +"/" + os.path.basename(token)
+        shutil.copy(token, dtoken)
+        out[id] = {'token': dtoken}
+
+        if os.path.isdir(dzip + "/verified_tokens/"+id):
+            vtokens = glob.glob(dzip + "/verified_tokens/"+id +"/*.token")
+            assert len(vtokens) <= 1
+            if len(vtokens) == 1:
+                out[id]['verified_token'] = vtokens[0]
+    return dzip, out
+
+
+
+
+def process_by_zip_file(learn_zip_file_path, output_xlsx=True, moss=True, docker_verify=True, instructor_grade_script=None):
+    # Automatic evaluation of tests.
+    # Moss
+    # Write to excel file
+    # Write to pkl file.
+    # hidden tests.
+    dzip, out = unzip(learn_zip_file_path)
+
+    if moss:
+        moss_check(dzip, out)
+
+    if docker_verify:
+        docker_verify_projects(learn_zip_file_path, instructor_grade_script=instructor_grade_script)
+    verbose = True
+    res = project_print_results(learn_zip_file_path,verbose=verbose)
+
+
+
+    a = 24
+    pass
+
+if __name__ == "__main__":
+    # Process a learn .zip file.
+
+
+
+    pass
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/dummy_handins.py b/src/unitgrade_private/pipelines/dummy_handins.py
index 895e13ebbe8a0d2bdd2c34f3286c26c1a8e6c72c..fa366f1632909115257a247ee7c9bd35a1e294da 100644
--- a/src/unitgrade_private/pipelines/dummy_handins.py
+++ b/src/unitgrade_private/pipelines/dummy_handins.py
@@ -1,9 +1,11 @@
 import os.path
 import os
 import shutil
+import numpy as np
+import glob
+import subprocess
 
-def make_dummies(zip_file_path="zip1.zip",
-                 , n_handins=3, screwups=4, student_base_dir=cdir+"/students", student_grade_file=cdir+"/students/intro_python/exam_grade.py"):
+def make_dummies(zip_file_path="zip1.zip", n_handins=3, screwups=4, student_base_dir=None, student_grade_file=None, instructor_base_dir=None):
     # I am dum-dum.
     dir = os.path.dirname(__file__)
     tmp = dir + "/tmp"
@@ -12,7 +14,99 @@ def make_dummies(zip_file_path="zip1.zip",
     os.mkdir(tmp)
     # now we got a temp dir.
     # Deploy to this dir and create handins. Turn it all into a .zip file and return it.
+    np.random.seed(42)
 
+    def copy_and_mutate():
+        if os.path.isdir(tmp + "/students"):
+            shutil.rmtree(tmp + "/students")
 
-    a = 234
-    pass
\ No newline at end of file
+
+        shutil.copytree(student_base_dir, tmp + "/students",dirs_exist_ok=True)
+        # Copy instructor files over and mutate.
+        for file in glob.glob(student_base_dir +"/**/*.py"):
+            # print(file)
+
+            rel = os.path.relpath(file, student_base_dir)
+            with open(tmp +"/students/"+rel, 'r') as f:
+                st_f = f.read()
+
+            with open(instructor_base_dir + "/"+os.path.relpath(file, student_base_dir), 'r') as f:
+                in_f = f.read()
+
+            from snipper.block_parsing import indent
+
+            if st_f == in_f:
+                continue
+            else:
+                # Take the instructor version and mutate it.
+                print("Messing up file", file)
+                in_mut = []
+                from snipper.block_parsing import indent
+                in_f_split =in_f.splitlines()
+                for k, l in enumerate(in_f_split):
+
+
+                    if k > 0 and len(indent(l)) > 0 and indent(l) == indent(in_f_split[k-1]):
+                        if np.random.rand() < 0.1:
+                            in_mut.append(indent(l) +"assert(False)")
+                    in_mut.append(l)
+
+                # "\n".join(in_mut)
+                with open(tmp + "/students/" + rel, 'w') as f:
+                    f.write("\n".join(in_mut))
+
+
+        module = ".".join(os.path.relpath(student_grade_file, student_base_dir)[:-3].split("/"))
+        cmd = f"cd {tmp}/students && python -m {module}"
+        print(cmd)
+        o = subprocess.run(cmd, shell=True, capture_output=True, check=True)
+        print(o)
+
+
+        token = glob.glob(os.path.dirname(tmp +"/students/"+os.path.relpath(student_grade_file, student_base_dir)) + "/*.token")[0]
+        print(token)
+        return token
+
+    names = ['Alice', 'Bob', 'Charlie', 'Doris', 'Ebert']
+
+    # def zipfolder(foldername, target_dir):
+    #     zipobj = zipfile.ZipFile(foldername + '.zip', 'w', zipfile.ZIP_DEFLATED)
+    #     rootlen = len(target_dir) + 1
+    #     for base, dirs, files in os.walk(target_dir):
+    #         for file in files:
+    #             fn = os.path.join(base, file)
+    #             zipobj.write(fn, fn[rootlen:])
+
+
+
+    for k in range(n_handins):
+        token = copy_and_mutate()
+
+        # Now make directory for handin and create the .zip file.
+
+        id = 221000 + k
+        handin_folder = f"116607-35260 - s{id} - {names[k % len(names)]} - 1 March, 2022 518 PM"
+        os.makedirs(tmp + "/zip/"+handin_folder)
+
+        shutil.copy(token, tmp + "/zip/"+handin_folder +"/"+os.path.basename(token))
+
+    shutil.make_archive(zip_file_path[:-4],
+                        'zip',
+                        tmp+ "/zip",
+                        '')
+
+    return zip_file_path
+
+
+    # zipfolder('zip1', tmp + "/zip/"+handin_folder)  # insert your variables here
+    # sys.exit()
+
+    #
+    # import zipfile
+    #
+    # with zipfile.ZipFile(zip_file_path, 'w') as zip:
+    #     zip.
+    #
+    #
+    # a = 234
+    # pass
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/process_65.py b/src/unitgrade_private/pipelines/process_65.py
deleted file mode 100644
index f838fa447d8ecc5472cf3bab45233fb8976aa960..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/process_65.py
+++ /dev/null
@@ -1,16 +0,0 @@
-
-def process_by_zip_file(learn_zip_file_path, output_xlsx=True, moss=True):
-    # Automatic evaluation of tests.
-    # Moss
-    # Write to excel file
-    # Write to pkl file.
-    # hidden tests.
-
-    pass
-
-if __name__ == "__main__":
-    # Process a learn .zip file.
-
-
-
-    pass
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/fractions.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/fractions.py
new file mode 100644
index 0000000000000000000000000000000000000000..a02d4fcca456701a4626bb560d6426da7f8e8465
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_course/fractions.py
@@ -0,0 +1,53 @@
+class Fraction: #!s
+    def __init__(self, n, m):
+        self.n = n
+        self.m = m
+
+    def __add__(self, other): #!f f should be the result of adding fraction 'other' to this fraction.
+        # Computes f = f1 + f2 (where f1 and f2 are both Fraction-objects and f1=self, f2=other) #!s
+        assert(False)
+        f = Fraction(other.m * self.n + other.n * self.m, self.n * self.m)
+        return f #!s
+
+    def __mul__(self, other): #!f f should be the result of multiplying fraction 'other' to this fraction.
+        # Overwrite to implement f = f1 * f2 #!s
+        f = Fraction(other.n * self.n, other.m*self.m)
+        assert(False)
+        return f #!s
+
+    def __truediv__(self, other): #!f f should be the result of dividing this fraction with 'other'
+        # Overwrite to implement f = f1/f2, or more specifically self/other. #!s
+        f = self.__mul__(Fraction(other.m, other.n))
+        return f #!s
+
+    def __str__(self):
+        """ Creates a string representation. You can use it as `print(str(Fraction(1,2))) to output 1/2"""
+        return f"{self.n} / {self.m}" #!s
+
+
+def from_string(s):
+    """ Convert the string s to a Fraction(a, b) object. """
+    if '+' in s: #!b
+        l = [from_string(ss.strip()) for ss in s.split("+")]
+        return l[0] + l[1]
+    if 'div' in s:
+        l = [from_string(ss.strip()) for ss in s.split("div")]
+        return l[0] / l[1]
+    if '*' in s:
+        l = [from_string(ss.strip()) for ss in s.split("*")]
+        return l[0] * l[1]
+    if '/' in s:
+        return Fraction(*[int(ss.strip()) for ss in s.split("/")]) #!b Compute and return a Fraction(a,b) object corresponding to s.
+
+
+if __name__ == "__main__": #!o=a
+    f1 = Fraction(1, 2)  # Represents 1/2 #!s=a
+    f2 = Fraction(3, 5) # Represents 3/5
+    print(f"Result of {f1} + {f2} is", f1+f2) #!s=a
+    #!o=a
+    # Now do some compound tests:
+    s = " 1 / 4 * 1 / 2" #!s=b #!o=b
+    print("Result of", s, "is", from_string(s))
+    assert(False)
+    s =  "5 / 2 div 10 / 3"
+    print("Result of", s, "is", from_string(s)) #!s=b #!o=b
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/fragment.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/fragment.py
new file mode 100644
index 0000000000000000000000000000000000000000..42495b68fc3da0bfd035a9640688db3e4a43e692
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_course/fragment.py
@@ -0,0 +1,17 @@
+
+
+from cpp_course.fractions import Fraction, from_string
+
+f1 = Fraction(1, 2)  # Represents 1/2  #!i
+f2 = Fraction(3, 5)  # Represents 3/5
+print(f"Result of {f1} + {f2} is", f1 + f2)
+
+# Now do some compound tests:
+s = " 1 / 4 * 1 / 2"
+print("Result of", s, "is", from_string(s))
+s = "5 / 2 div 10 / 3"
+print("Result of", s, "is", from_string(s))
+pass
+#!i
+
+
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cc6174659c097f70516cbf045da3ef8eb51ad8c
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6.py
@@ -0,0 +1,54 @@
+from unitgrade import UTestCase, Report, cache
+from cpp_course.fractions import from_string, Fraction
+
+class Fractions_from_string(UTestCase):
+    def test_from_string_manual(self):
+        self.assertEqual(str(from_string("2 / 3 + 4 / 5")), "22/6")
+
+    def test_from_string_smarter(self):
+        self.assertEqualC(str(from_string("2 / 3 + 4 / 5")))
+
+    @cache
+    def output(self, problem):
+        return from_string(problem)
+
+    def test_from_string_smartest(self):
+        problems = """
+        2 / 3 + 4 / 5
+        1 / 2 * 3 / 4
+        1 / 2 div 1 / 2
+        1 / 4 + 1 / 2
+        1 / 2 * 2 / 4
+        1 / 2 div 4 / 2"""
+        print("\nTesting a bunch of problems...")
+        for l in problems.strip().splitlines():
+            l = l.strip() # Remove trailing spaces
+            print(f"Testing from_string({l}), output ought to be: {self.output(l)}")
+            self.assertEqualC(str(from_string(l))) # Actually perform the test.
+
+class Fractions_Basics(UTestCase):
+    def test_addition(self):
+        f1 = Fraction(1, 2)  # Represents 1/2
+        f2 = Fraction(3, 5)  # Represents 3/5
+        self.assertEqualC(str(f1 + f2))
+
+    def test_multiplication(self):
+        f1 = Fraction(1, 2)  # Represents 1/2
+        f2 = Fraction(3, 5)  # Represents 3/5
+        self.assertEqualC(str(f1 * f2))
+
+    def test_division(self):
+        f1 = Fraction(1, 2)  # Represents 1/2
+        f2 = Fraction(3, 5)  # Represents 3/5
+        self.assertEqualC(str(f1 / f2))
+
+
+import cpp_course
+class Week6(Report):
+    title = "02393 Programming in C++: Problem set 6"
+    pack_imports = [cpp_course]
+    questions = [(Fractions_from_string, 10), (Fractions_Basics, 10)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Week6())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6_grade.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd11dbb77c1ef8459f783a76addc835596e4ecd1
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6_grade.py
@@ -0,0 +1,4 @@
+# cpp_course/tests_ex6.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem1_vectors.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem1_vectors.py
new file mode 100644
index 0000000000000000000000000000000000000000..e986ff9bd9bb473175daa418ddc928015373e181
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem1_vectors.py
@@ -0,0 +1,33 @@
+class Vector:
+    def __init__(self, x, y):
+        self.x, self.y = x, y
+
+def createField(m, n, v): #!f Create and return a field of the form [ [v11, v12,...], [v21, v22,...],...] here
+    return [[Vector(v.x, v.y) for j in range(n)] for i in range(m)]
+
+def displayField(A): #!f Compute and return a string representation of A (see problem description)
+    return "\n".join([' '.join([f"({v.x},{v.y})" for v in c]) for c in A])
+
+def addFields(A, B): #!f Create and return a new field corresponding to A+B (element-wise)
+    return [ [Vector(v1.x + v2.x, v1.y+v2.y) for v1, v2 in zip(c1,c2) ] for c1,c2 in zip(A,B)]
+
+def scaleField(A, s):  #!f Create and return a new field corresponding to A*s (element-wise)
+    return [[Vector(v1.x * s, v1.y*s) for v1 in c1] for c1 in A]
+
+if __name__ == "__main__":
+    A = createField(3, 3, Vector(1,2)) #!s #!o
+    assert(False)
+    A[1][1] = Vector(2,2)
+    print(displayField(A))
+    assert(False)
+    print( )
+    B = createField(3, 3, Vector(2,-2))
+    B[0][0] = B[2][2] = Vector(9, 8)
+    print(displayField(B))
+    print( )
+    assert(False)
+    print(displayField(addFields(A, B)))
+    assert(False)
+    print("Result of scaling A by 2")
+    print(displayField(scaleField(A, 2))) #!s #!o
+
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem2_rle.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem2_rle.py
new file mode 100644
index 0000000000000000000000000000000000000000..16129eadcc900777d1cde83665212ac610b684e4
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem2_rle.py
@@ -0,0 +1,41 @@
+class Elem:
+    def __init__(self, value, times, next):
+        self.value = value
+        self.times = times
+        self.next = next
+
+def displayRLEList(l):
+    if l is None: return ""
+    return f" {l.value} (x{l.times})" +  displayRLEList(l.next)
+
+def reverse(l): #!f
+    prev = None
+    current = l
+    while current is not None:
+        n = current.next
+        prev = Elem(current.value, current.times, prev)
+        assert(False)
+        current = n
+    return prev
+
+def concatenate(l1, l2): #!f
+    hd = l1
+    assert(False)
+    while l1.next is not None:
+        l1 = l1.next
+    l1.next = l2
+    return hd
+
+def rle_sum(l): #!f
+    return 0 if l is None else (l.value * l.times + rle_sum(l.next))
+
+if __name__ == "__main__":
+    l1 = Elem(4, 10, Elem(12, 6, Elem(10, 5, None)))
+    l2 = Elem(1, 1, Elem(2, 2, Elem(3, 3, None)))
+
+    print("The RLE list is: ", displayRLEList(l1))
+    print("Other is: ", displayRLEList(l2))
+
+    print("The reversed list is: ", displayRLEList(reverse(l1)))
+    print("After concatenation, the list is: ", displayRLEList(concatenate(l1, l2)))
+    print("The sum of its elements is: ", rle_sum(l1))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem3_groceries.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem3_groceries.py
new file mode 100644
index 0000000000000000000000000000000000000000..97401dc33fdbc39b1d2a284810f3579e4deb4bba
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem3_groceries.py
@@ -0,0 +1,33 @@
+class GroceryList:
+    def __init__(self):
+        # Populate the items:
+        self.items = {'Lasagne': (1, "With eggs if available"), "Salmon": (500, "Smoked if available"),
+                      "Spinach": (300, "Fresh"),                "Dessert": (8, "Maybe lagkage?"), }
+
+    def add(self, name, quantity=1, notes=""): #!f
+        if name in self.items:
+            item = self.items[name]
+            self.items[name] = (item[0] + quantity, item[1] + ";" + notes)
+        else:
+            self.items[name] = (quantity, notes)
+
+    def remove(self, name, quantity=1): #!f
+        if name not in self.items:
+            return False
+        else:
+            self.items[name] = (self.items[name][0]-quantity, self.items[name][1])
+            if self.items[name][0] <= 0:
+                del self.items[name]
+            return True
+
+    def copyEntry(self, name, new_name): #!f
+        if name not in self.items or new_name in self.items:
+            return False
+        else:
+            self.items[new_name] = self.items[name]
+            return True
+
+    def display(self): # Don't edit this function; it is used for the test.
+        s = [f"> {name=};{quantity=};{notes=}" for name, (quantity, notes) in self.items.items()]
+        print("\n".join(s))
+        return s
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem4_filter.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem4_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..f66aaf6fff30680d14e186d84e1a4473988686ed
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem4_filter.py
@@ -0,0 +1,39 @@
+class Buffer:
+    def __init__(self, default=-999): #!f
+        self.buffer = []
+        self.known = set()
+        self.default = default
+
+    def write(self, n): #!f
+        if n not in self.known:
+            self.buffer.insert(0, n)
+            self.known.add(n)
+
+    def reset(self): #!f
+        self.buffer.clear()
+        self.known.clear()
+
+    def occupancy(self): #!f
+        return len(set(self.buffer))
+
+    def read(self): #!f
+        return self.buffer.pop() if len(self.buffer) > 0 else self.default
+
+if __name__ == "__main__":
+    b = Buffer()
+    print("Current buffer occupancy: ", b.occupancy())
+    print("Reading from the buffer returns: ", b.read())
+
+    for i in range(10):
+        b.write(i*10)
+
+    print("Current buffer occupancy: ", b.occupancy())
+    for i in range(10):
+        b.write(20)
+    print("Current buffer occupancy: ", b.occupancy())
+    for _ in range(3):
+        print("Reading from the buffer returns: ", b.read())
+    print("Current buffer occupancy: ", b.occupancy())
+    b.reset()
+    print("Current buffer occupancy: ", b.occupancy())
+    print("Reading from the buffer returns: ", b.read())
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68bfbe3c62c770d66c894a06112d2d30f9c4b80
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam.py
@@ -0,0 +1,263 @@
+from unitgrade import UTestCase, Report
+import cpp_exam
+from cpp_exam.problem1_vectors import Vector, createField, displayField, scaleField, addFields
+from cpp_exam.problem2_rle import Elem, rle_sum, reverse, concatenate, displayRLEList
+from cpp_exam.problem3_groceries import GroceryList
+from cpp_exam.problem4_filter import Buffer
+
+# class Q1Vectors_Examples(UTestCase):
+#     def test_correct_format(self):
+#         """ Test your field is in the right format.
+#             Hints:
+#                 * If this test fails, all subsequent tests will fail. Make sure it works!
+#         """
+#         A = createField(3, 3, Vector(1, 2))
+#         self.assertEqual(len(A), 3) # Check there are 3 rows in A
+#         self.assertEqual(len(A[0]), 3)  # Check there are 3 columns in A
+#         v = A[1][1]                 # Should be a Vector(1, 2) element.
+#         self.assertEqual(v.x, 1)  # Check that first coordinate is 1
+#         self.assertEqual(v.y, 2)  # Check that first coordinate is 1
+#
+#     def test_displayField(self):
+#         A = createField(3, 3, Vector(1, 2))
+#         A[1][1] = Vector(2, 2)
+#         self.assertEqualC(displayField(A).strip())
+#
+#     def test_addFields(self):
+#         A = createField(3, 3, Vector(1, 2))
+#         A[1][1] = Vector(2, 2)
+#         B = createField(3, 3, Vector(2, -2))
+#         B[0][0] = B[2][2] = Vector(9, 8)
+#         self.assertEqualC(displayField(addFields(A, B)).strip())
+#
+#     def test_scaleFields(self):
+#         A = createField(3, 3, Vector(1, 2))
+#         A[1][1] = Vector(2, 2)
+#         self.assertEqualC(displayField(scaleField(A, 2)).strip())
+
+def vector2string(v):
+    return f"({v.x},{v.y})"
+
+def check_field(self, A):
+    print("\nChecking size and type of field...")
+    self.assertIsInstance(A, list)
+    self.assertIsInstance(A[0], list)
+    self.assertIsInstance(A[0][0], Vector)
+    print("Checking all elements of field...")
+    for i, r in enumerate(A):
+        for j, a in enumerate(r):
+            # if i > 3 or j > 3: continue
+            print(f"Checking that A[{i}][{j}] = {self.get_expected_test_value()};", "your value was", vector2string(A[i][j]))
+            self.assertEqualC(vector2string(A[i][j]))
+
+class Q1Vectors(UTestCase):
+    def test_correct_format(self):
+        """ Test your field is in the right format.
+            Hints:
+                * If this test fails, all subsequent tests will fail. Make sure it works!
+        """
+        A = createField(3, 3, Vector(1, 2))
+        self.assertEqual(len(A), 3) # Check there are 3 rows in A
+        self.assertEqual(len(A[0]), 3)  # Check there are 3 columns in A
+        v = A[1][1]                 # Should be a Vector(1, 2) element.
+        self.assertEqual(v.x, 1)  # Check that first coordinate is 1
+        self.assertEqual(v.y, 2)  # Check that first coordinate is 1
+
+    def test_createField_small(self):
+        # Test a 4x4 field
+        check_field(self, createField(4, 4, Vector(1,2)))
+        # Test an 8x8 field
+        check_field(self, createField(8, 8, Vector(3,8)))
+
+    def test_display_field(self):
+        A = createField(4, 3, Vector(1,2))
+        A[0][0] = A[1][1] = A[2][2] = Vector(0,0)
+        self.assertEqualC(displayField(A))
+
+        B = createField(6,8, Vector(3, -9))
+        B[0][1] = B[2][1] = B[3][2] = B[4][5] = B[5][6] = Vector(0,0)
+        self.assertEqualC(displayField(B))
+
+    def test_add_fields(self):
+        A = createField(4, 3, Vector(1,2))
+        B = createField(4, 3, Vector(3, -9))
+        check_field(self, addFields(A, B))
+
+    def test_scale_fields(self):
+        A = createField(2, 4, Vector(1,2))
+        check_field(self, scaleField(A, 2))
+        A = createField(6, 6, Vector(3, 1))
+        check_field(self, scaleField(A, 3))
+
+def make_rle1():
+    return Elem(4, 10, Elem(12, 6, Elem(10, 5, None)))
+
+def make_rle2():
+    return Elem(4, 2, Elem(5, 3, Elem(3, 5, None)))
+
+def make_rle3():
+    return Elem(6, 3, Elem(7, 5, Elem(8, 5, None)))
+
+class Q2RLE(UTestCase):
+    def test_reverse_empty(self):
+        self.assertEqual(reverse(None), None)
+
+    def test_reverse(self):
+        self.assertEqualC(displayRLEList(reverse(make_rle1())))
+        l = make_rle1() # Test reversal of the tail
+        self.assertEqualC(displayRLEList(reverse(l.next)))
+
+    def test_concatenate_with_empty(self):
+        l = make_rle1()
+        print(f"Concatenating {displayRLEList(l)} with empty list")
+        self.assertEqualC(displayRLEList(concatenate(l, None)))
+
+        l = make_rle2()
+        print(f"Concatenating {displayRLEList(l)} with empty list")
+        self.assertEqualC(displayRLEList(concatenate(l, None)))
+
+    def test_concatenate_with_another(self):
+        l1, l2 = make_rle1(), make_rle2()
+        print(f"Concatenating {displayRLEList(l1)} with {displayRLEList(l2)}")
+        self.assertEqualC(displayRLEList(concatenate(l1, l2)))
+
+    def test_concatenate_three_lists(self):
+        l1, l2, l3 = make_rle1(), make_rle2(), make_rle3()
+        print(f"Concatenating {displayRLEList(l1)} with {displayRLEList(l2)} and {displayRLEList(l3)}")
+        self.assertEqualC(displayRLEList(concatenate(l1, concatenate(l2, l3))))
+
+    def test_sum_empty(self):
+        print(f"Computing sum of empty list")
+        self.assertEqual(rle_sum(None), 0)
+
+    def test_sum_nonempty(self):
+        for l in [ make_rle1(),  make_rle2(),  make_rle3()]:
+            print(f"Computing sum of {displayRLEList(l)}")
+            self.assertEqualC(rle_sum(l), 0)
+
+class Q3Groceries(UTestCase):
+    def test_add(self):
+        gl = GroceryList()
+        print("Initial list")
+        gl.display()
+        print("After adding cheddar:")
+        gl.add("Cheddar", 500, "Not too mature")
+        gl.display()
+        print("After adding more spinach:")
+        gl.add("Spinach", 200, "Baby spinach if available")
+        gl.display()
+        print("After adding even more spinach:")
+        gl.add("Spinach", 200, "Frozen is OK")
+        gl.display()
+
+    def test_remove1(self):
+        gl = GroceryList()
+        gl.display()
+        print("After removing spinach")
+        self.assertTrue(gl.remove("Spinach", 200))
+        self.assertEqualC(gl.display())
+
+        print("After removing more spinach")
+        self.assertTrue(gl.remove("Spinach", 100))
+        self.assertEqualC(gl.display())
+
+        print("Trying to remove even more spinach:")
+        self.assertFalse(gl.remove("Spinach", 100))
+
+        print("Trying to remove cheddar:")
+        self.assertFalse(gl.remove("Cheddar", 100))
+
+    def test_copy(self):
+        gl = GroceryList()
+        print("Initial grocery list:")
+        gl.display()
+        print("After copying spinach into baby spinach:")
+        self.assertTrue(gl.copyEntry("Spinach", "Baby spinach"))
+        self.assertEqualC(gl.display())
+        print("After copying baby spinach into dessert:")
+        self.assertFalse(gl.copyEntry("Baby spinach", "Dessert"))
+        print("After copying Cheddar into Spinach")
+        self.assertFalse( gl.copyEntry("Cheddar", "Spinach"))
+
+class Q4FilterBuffer(UTestCase):
+    def test_if_compiles(self):
+        b = Buffer()
+        b.write(42)
+        b.write(42)
+        b.occupancy()
+        b.read()
+        print("None of the functions crashed! Congrats!")
+
+    def test_occupancy(self):
+        b = Buffer()
+        print("Current occupancy", b.occupancy())
+        self.assertEqual(b.occupancy(), 0)
+        for i in range(7):
+            b.write(i*10)
+        print("Current buffer occupancy: ", b.occupancy())
+        self.assertEqual(b.occupancy(), 7)
+        for i in range(5, 10):
+            b.write(i*10)
+        print("Current buffer occupancy: ", b.occupancy())
+        self.assertEqual(b.occupancy(), 10)
+
+    def test_read(self):
+        b = Buffer()
+        print("Current buffer occupancy: ", b.occupancy())
+        print(f"Reading from empty buffer should return default value of {b.default}")
+        self.assertEqual(b.read(), -999)
+
+        for i in range(5):
+            b.write(i*10)
+
+        print("Current buffer occupancy: ", b.occupancy() )
+        self.assertEqual(b.occupancy(), 5)
+        for i in range(3):
+            r = b.read()
+            print("Reading from the buffer returns: ", r)
+            self.assertEqual(r, i*10)
+        print("Current buffer occupancy: ", b.occupancy() )
+        self.assertEqualC(b.occupancy())
+
+        for i in range(8):
+            b.write(i * 10)
+        print("Current buffer occupancy: ", b.occupancy())
+        self.assertEqualC(b.occupancy())
+
+        for i in range(6):
+            r = b.read()
+            print("Reading from the buffer returns: ", r)
+            self.assertEqualC(r)
+
+    def test_reset(self):
+        b = Buffer()
+
+        for i in range(5):
+            b.write(i*10)
+        print("Current buffer occupancy: ", b.occupancy() )
+        self.assertEqual(b.occupancy(), 5)
+        for i in range(3):
+            r = b.read()
+            print("Reading from the buffer returns: ", r)
+            self.assertEqual(r, i*10)
+        print("Current buffer occupancy: ", b.occupancy() )
+        self.assertEqualC(b.occupancy())
+        b.reset()
+        for i in range(8):
+            b.write(i * 10)
+        print("Current buffer occupancy: ", b.occupancy())
+        self.assertEqualC(b.occupancy())
+
+        for i in range(3):
+            r = b.read()
+            print("Reading from the buffer returns: ", r)
+            self.assertEqualC(r)
+
+class ExamMay2021(Report):
+    title = "Programming in C++: Exam May 2021"
+    pack_imports = [cpp_exam]
+    questions = [(Q1Vectors, 25), (Q2RLE, 25), (Q3Groceries, 25), (Q4FilterBuffer, 25)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(ExamMay2021())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam_grade.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..d09bb1329d42fc29cd42b16524e54449d5669ae7
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam_grade.py
@@ -0,0 +1,4 @@
+# cpp_exam/tests_exam.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_Examples.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_Examples.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_addFields.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_addFields.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_createField.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_createField.pkl
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/exam.py b/src/unitgrade_private/pipelines/tmp/students/intro_python/exam.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8cf1b269e2829ae11a1282b4fca1be18ad49acb
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/intro_python/exam.py
@@ -0,0 +1,62 @@
+import numpy as np
+from unitgrade import UTestCase, Report, hide
+import intro_python
+from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
+
+class Q1_WaterHeight(UTestCase):
+    def test1(self):
+        h0 = 5
+        r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
+        h = water_height(h0, r)
+        print("Water height computed to be", h, "should be", self.get_expected_test_value())
+        self.assertEqual(h, 3.0) # Check the height is 3.0
+
+
+class Q2_AstronomicalSeason(UTestCase):
+    def test_seasons(self):
+        season = astronomical_season('09/12-2020')
+        print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(season)
+
+
+
+class Q3_TimeAngle(UTestCase):
+    def test_angle(self):
+        a = time_angle(hour=8, minute=20)
+        print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
+        self.assertEqualC(a)
+
+
+class Q4_TicTacToe(UTestCase):
+    def test_tic_tac(self):
+        board = np.array([[2, 1, 1],
+                          [1, 1, 2],
+                          [2, 0, 0]])
+        score = tictactoe(board)
+        print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
+        self.assertEqualC(score)
+
+
+
+class Q5_StandardizeAddress(UTestCase):
+    def test_standardize_address(self):
+        s = standardize_address('New York 10001')
+        print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
+        assert(False)
+        self.assertEqualC(s)
+
+
+
+
+class Exam2021(Report):
+    title = "Introduction to Python: Exam spring 2021"
+    pack_imports = [intro_python]
+    questions = [(Q1_WaterHeight, 20),
+                 (Q2_AstronomicalSeason, 20),
+                 (Q3_TimeAngle, 20),
+                 (Q4_TicTacToe, 20),
+                 (Q5_StandardizeAddress, 20)]
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/exam_grade.py b/src/unitgrade_private/pipelines/tmp/students/intro_python/exam_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..67b272c3fa4478273b3f9ea2a69d03e824a5ae17
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/intro_python/exam_grade.py
@@ -0,0 +1,4 @@
+# intro_python/exam.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/problems.py b/src/unitgrade_private/pipelines/tmp/students/intro_python/problems.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6f738567b918f4abedbb39d563de5b306008c7d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/intro_python/problems.py
@@ -0,0 +1,87 @@
+import numpy as np
+
+def astronomical_season(date):
+    """ Problem 1. Given a date (as a string) return the season (as a string)
+    Hints:
+        * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
+        * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
+    """
+    dd = int(date[0:2]) #!b
+    mm = int(date[3:5])
+    if mm<3 or (mm==3 and dd<20):
+        season = 'winter'
+    elif mm<6 or (mm==6 and dd<21):
+        season = 'spring'
+    elif mm<9 or (mm==9 and dd<23):
+        season = 'summer'
+    elif mm<12 or (mm==12 and dd<21):
+        season = 'autumn'
+    else:
+        season = 'winter' #!b Compute the season here.
+    return season
+
+
+def standardize_address(address):
+    """ Given an address, return a standardized version (see question text)
+
+    Hints:
+        * The function s.isnumeric() may be of help to check if a string s is a number.
+    """
+    s = address.replace('_', ' ') #!b
+    if not s[0].isnumeric():
+        i = s[-1::-1].find(' ')
+        i = len(s) - 1 - i
+        s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
+    return s
+
+def tictactoe(board):
+    """ Given a tic-tac-toc board configuration, return whether it is valid or not.
+
+    Hints:
+        * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
+        * You can 'flatten' an array using A.ravel().
+    """
+    def wins(b): #!b
+        return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
+
+    ones_wins = wins(board == 1)
+    twos_wins = wins(board == 2)
+    d = np.sum(board == 1) - np.sum(board == 2)  # 0 or 1 for a valid board
+    assert(False)
+    if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
+        score = -1
+    elif ones_wins:
+        score = 1
+    elif twos_wins:
+        score = 2
+    else:
+        score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
+    return score
+
+def time_angle(hour, minute):
+    """ Given hours and minutes (integers), return the angle of the small hand.
+
+    Hints:
+        * The angle is between 0 and 360.
+        * Noon and midnight, 12:00 and 24:00, is both 0
+        * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
+    """
+    hour = hour % 12 #!b
+    hour_hand = 360 * (hour + minute / 60) / 12
+    minute_hand = 360 * minute / 60
+    a = hour_hand - minute_hand
+    assert(False)
+    if a < 0:
+        a = a + 360 #!b Compute the angle of the small hand here
+    return a
+
+def water_height(h0, r):
+    """ Problem 1: Compute the water height.
+
+    Hints:
+        * Remember the height must be a non-negative number.
+    """
+    h = h0 #!b
+    for ri in r:
+        h = max(h + ri - 2, 0) #!b Compute the water height h here.
+    return h
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Problem1.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Problem1.pkl
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index 0000000000000000000000000000000000000000..15baef336eff2b1034fdb3e6def2a77b7b19b5ac
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q1_WaterHeight.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q1_WaterHeight.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..834d08b7bbdcf8ebf2e06cfe57953e687871396a
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2d677c3561d435b085828c376c30118f918b7c5d
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..8528aeef3c8c029a9a8af2b1bc198d19a0463555
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..163e3b6bbf380c2ec67ca64c32d89bee735a97be
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..a55324933d96a979dce39176c27427e4ef8b5ea2
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..d323d4cf41dd5b63d5f45f495924e2f57c13bc78
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diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..1a0ef426f0359ec6fd12754b46385cba59ec162f
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token	
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
+0dcd42292c9bd80c13f72336d4f23477a2a820d658f5e7db02fa7a64643fca9057801c426f41331c2ea82045db6240c2f17da1521ad44b0e17e128fc5bbbb7fa 36456
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
+./Td6WFoAAATm1rRGAgAhARYAAAB0L+Wj4K34aopdAEABDnzn8+Mi0wiOhiLO3TT1mshp9WIBZ+1VKaDRHlt6hGTEJXvfkQOedgW15lwYfuVJeb4NO6WfI4RWi7tL6siMJ0OFM3TvKWc8faN2WHNd62fez7fO4C4Jj4sOD0iKx8VzE6GpLIH
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..68797b0b56e3e0418752dee0d9f778ccfac9ae26
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token	
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token
new file mode 100644
index 0000000000000000000000000000000000000000..74ab5a3d17172fbf232bac79b3b1e04cd6252cc7
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token	
@@ -0,0 +1,207 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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